evaluation of automatic feature extraction …library.iugaza.edu.ps/thesis/110012.pdf · pca...
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The Islamic University Gaza
Higher Education Deanship
Faculty of Engineering
Civil Engineering Department
Infrastructure Program
غزة – اإلسالمية الجامعة
العليا الدراسات عمادة
الهندسة كلية
قسم الهندسة المدنية
هندسة البنى التحتية
بطريقة اتوماتيكية الجويةاستخراج المعالم من الصورتقييم طرق
EVALUATION OF AUTOMATIC FEATURE EXTRACTION
TECHNIQUES FROM IMAGERY
Submitted by:
Wesam A. Alashqar
Supervised by:
Dr. Maher A. El-Hallaq
A Thesis Submitted in Partial Fulfillment of Requirements for the Degree of Master of
Science in Infrastructure - Civil Engineering.
م ۲۰۱۳-هـ ۱٤۳٤
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DEDICATION
To my parents, who gave me life twice, once when I was born, and again when they
thought I have to go to school..
To martyrs: my uncle Abed Al Azeez and uncle Mohammad (Abo Amer) and my
cousin Mohammad (Abo Youssef)
To my wife, Feda, who always pushed me to finish this thesis..
To my sons, Mohammad and Ahmed, whom I do all of this for..
To my brothers, Hosam, Ehab and Eyad and, and my sisters, Samaher, Sabreen, and
Hadeel..
To my teachers, along my academic trip extending over 20 years..
To my country, Palestine, united Palestine…
I dedicate this work.
ii
ACKNOWLEDGEMENT
I would like to express my deep acknowledgement to everyone who helped me finish
this work, especially Dr. Maher El-Hallaq, my thesis supervisor, who always gave
me spiritual support and technical guidance during the research work.
I would thank the Municipality of Gaza for providing data (Maps) related to research.
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7Bملخص الدراسة
في المعقدة المهام إلنجاز بعد عن االستشعار بيانات استخدام إلى الحاجة شهدت الماضية القليلة السنوات مدى على
الجوية الصور ألن صعبة مهمة هي الصور من الخرائط رسم معالم استخراجتعتبر .استخراج المعالم من الصور
المعلومات نظم أنشطة من للعديد جدا تعتبر مهمة المعالم من الصور استخراج. وغامضة ومعقدة، بطبيعتها، صاخبة
تعتمد العملية هذه. المكانية الجغرافية البيانات تكامل واالرجاع الجغرافي وكذلك التحديث، مثل GIS الجغرافية
مكلفة عملية GIS بيانات نظم المعلومات الجغرافية قواعد تطوير يجعل مما البشرية، العمالة على كبيرا اعتمادا
الوقت من كبير بشكل تقلل أن يمكن المعالم بشكل آلي استخراج امكانية. يدويا بها القيام عند طويال وقتا وتستغرق
. البيانات قواعد وتطوير وتحديثها، البيانات على الحصول في والتكلفة
) GISم المعلومات الجغرافية (ستخدم نظبابلديات قطاع غزة والمؤسسات سنوات العشر األخيرة، بدأت بعض في ال
المعالم تعامل مع البيانات المتوفرة من الصور الجوية واستخراج الأصبح من الضروري في المشاريع. ولذلك
فلسطين كل الطرق المستخدمة في استخراج بر الستخدامها في التحليل والتخطيط وصنع القرار. في الوقت الحاض
).الرسم اليدويتقليدية (ال تزال المعالم من الصور الجوية
. المعالم من الصور ستخراجإل المستخدمة الصور أنواع عن عامة لمحة وتقديم تم استعراض األطروحة هذه في
، اإلجراءات لهذه والنوعية الكمية للدقة تقييم مع المعالم من الصور ستخراجإل المستخدمة األساليب وأيضا
مثل عدد من التقنيات المحسنة والتى تم تطويرها واستخدامها في بعض البرامجوتقييم دراسة باالضافة إلى
ERDAS 2013, ENVI 5.0, Barista 2.3.1 استخراج المعالم بشكل آلي مع دراسة يةمع دراسة سير عمل
.ومقارنة النتائج
صى الباحث بالعمل على تطوير أو تحسين خوارزميات ترفع من جودة البيانات التى يتم استخراجها في النهاية أو
.بقدر المستطاع والتعاون من فرق بحثة محلية وعالمية في هذا المجال
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ABSTRACT
Features extraction from raster images is very important of many GIS activities such as
GIS updating, geo-referencing, and geospatial data integration. This process depends
heavily on human labor, which makes GIS database development an expensive and
time-consuming operation when performed manually. Automated feature extraction can
significantly reduce the time and cost of data acquisition and update, database
development and turnaround time.
For the last ten years, some of the Gaza Strip municipalities and institutions have begun
using geographic information system (GIS) in their projects. Therefore, it was become
necessary to deal with available data from aerial images and extract features to be used
in analysis, planning and decision-making. Nowadays in Palestine, all methods used in
feature extraction are still traditional (manual methods).
Recently, the need for using remote sensing data to accomplish the complex task of
automatic extraction of features has significantly increased. Among the sensor systems
currently used for mapping can be highlighted the recent launches of new orbital
satellites. Extracting cartographic objects from images is a difficult task because aerial
images are inherently noisy, complex, and ambiguous.
This thesis reviews and provides an overview of the types of imagery being used for
feature extraction. It also describes the methods used for feature extraction as well as
the quantitative and qualitative accuracy assessment of these procedures. Number of
optimization techniques that have been developed in stand-alone programs are studied
such as ERDAS Imagine, ENVI and Barista to automate the extraction with evaluating
and comparing the feature extraction workflow of these programs and feature extraction
results.
Finally, it is recommended to continue working on the development or improvement of
existing algorithms to enhance the percentage of accuracy and speed of data, which are
extracted as much as possible and the cooperation with local and global teams in this
field.
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TABLE OF CONTENTS
DEDICATION ................................................................................................................. i
ACKNOWLEDGEMENT .............................................................................................. ii
الدراسة ملخص ...................................................................................................................... iii
ABSTRACT .................................................................................................................... iv
TABLE OF CONTENTS ............................................................................................... v
LIST OF ABBREVIATIONS ....................................................................................... ix
LIST OF TABLES .......................................................................................................... x
LIST OF FIGURES ....................................................................................................... xi
1 CHAPTER 1: INTRODUCTION
1.1 Scope ................................................................................................................. 1
1.2 Background ....................................................................................................... 1
1.3 Problem Statement ............................................................................................ 2
1.4 Research Aim and Objectives ........................................................................... 2
1.5 Methodology ..................................................................................................... 3
1.6 Research Structure ............................................................................................ 4
2 CHAPTER 2: REMOTE SENSING & DIGITAL IMAGE PROCESSING
2.1 Scope ................................................................................................................. 5
2.2 Remote Sensing ................................................................................................ 5
2.2.1 Historic Overview ....................................................................................... 5
2.2.2 Principles of Remote Sensing ..................................................................... 6
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2.2.3 Electromagnetic Radiation .......................................................................... 8
2.2.4 Electromagnetic Spectrum .......................................................................... 8
2.2.5 Sensors and Platforms ................................................................................. 9
2.2.5.1 Type of Satellite Sensors ...................................................................... 9
2.2.5.2 Satellite Sensor Characteristics ........................................................... 10
2.3 Digital Image Processing ................................................................................ 12
2.3.1 Pre-Processing .......................................................................................... 13
2.3.2 Image Enhancement .................................................................................. 15
2.3.3 Image Transformation ............................................................................... 15
2.3.4 Image Classification and Analysis ............................................................ 16
2.3.4.1 Supervised Classification .................................................................... 17
2.3.4.2 Unsupervised Classification ............................................................... 18
2.3.5 Available Feature Extraction Methods ..................................................... 19
2.3.5.1 Manual Digitizing ............................................................................... 19
2.3.5.2 Automatic Digitizing .......................................................................... 20
2.3.5.2.1 Object-Based Feature Extraction .................................................. 26
2.3.5.2.2 Pixel-Based Feature Extraction ..................................................... 27
2.4 Conclusion ...................................................................................................... 35
3 CHAPTER 3: LITERATURE REVIEW
3.1 Scope ............................................................................................................... 36
3.2 Background ..................................................................................................... 36
3.3 Types of Imagery Used in Feature Extraction ................................................ 38
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3.4 Feature Extraction ........................................................................................... 39
3.4.1 Manual Versus Automated Extraction ...................................................... 39
3.4.2 A Feature Model ....................................................................................... 40
3.5 Techniques for Feature Extraction .................................................................. 41
3.5.1 Mathematical Morphology ....................................................................... 41
3.5.2 Hough Transform ...................................................................................... 42
3.5.3 Multi-Resolution Techniques ................................................................... 44
3.5.4 Template Matching ................................................................................... 44
3.5.5 Dynamic Programming ............................................................................. 45
3.5.6 Particle Swarm Optimization .................................................................... 46
3.5.7 Pixel Swapping ......................................................................................... 47
3.5.8 LSB Snake ................................................................................................ 47
3.5.9 Edge Detection .......................................................................................... 48
3.6 Knowledge Integration ................................................................................... 49
3.7 Classification-based Feature Extraction ......................................................... 50
3.8 Assessing Feature Extraction Techniques ...................................................... 50
3.9 Conclusion ...................................................................................................... 51
4 CHAPTER 4: METHODS EVALUATION
4.1 Scope ............................................................................................................... 52
4.2 Data Collection and Preparation .................................................................... 52
4.2.1 Data Collection ......................................................................................... 52
4.2.2 Image Pre-Processing ............................................................................... 54
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4.2.2.1 Geometric Correction ......................................................................... 54
4.2.2.2 Radiometric Correction ....................................................................... 55
4.2.2.3 Image Enhancement ............................................................................ 56
4.3 Feature Extraction Methods ............................................................................ 57
4.3.1 Manual Digitizing ..................................................................................... 57
4.3.2 Automatic Digitizing ................................................................................ 60
4.3.2.1 Object-Based Feature Extraction ........................................................ 60
4.3.2.2 Pixel-Based Feature Extraction .......................................................... 71
4.4 Results and Discussion ................................................................................... 73
5 CHAPTER 5: CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion ...................................................................................................... 79
5.2 Recommendations ........................................................................................... 79
REFERENCES .............................................................................................................. 80
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LIST OF ABBREVIATIONS
AVIRIS Airborne Visible InfraRed Imaging Spectrometer
CAD Computer Aided Design
CCM Canada Centre for Mapping
CIR Colour Infrared
DN Digital Number
ENVI® The Environment for Visualizing Images
FOV Field of View
GCPs Ground Control Points
GIS Geographic Information System
GSD Ground Sample Distance
HT Hough Transform
HYDICE HYperspectral Digital Imagery Collection Experiment
IFOV Instantaneous Field of View
KNN K Nearest Neighbor
LiDAR Light Detection And Ranging Data
MM Mathematical Morphology
NASA National Aeronautics and Space Administration
NDVI Difference Vegetation Index
PCA Principal Components Analysis
PSO Particle Swarm Optimization
SAR Synthetic-Aperture Radar
SFP Single Feature Probability
SVM Support Vector Machine
TM Landsat Thematic Mapper
USGS U.S. Geological Survey
VLS Visual Learning Systems
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LIST OF TABLES
Table 2.1: Characteristics of various optical remote sensing systems.. .......................... 12
Table 4.1: Results of feature extraction methods ………………………….….……….76
Table 4.2: Comparison between area of extracted sample of buildings ......................... 76
Table 4.3: Time spent to extract features from images by different methods ................ 77
Table 4.4: Summary of overall comparison between feature extraction methods ......... 77
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LIST OF FIGURES
Figure 1.1: Methodology flowchart .................................................................................. 3
Figure 2.1: Elements of remote sensing system ............................................................... 7
Figure 2.2: Electromagnetic radiation components .......................................................... 8
Figure 2.3: Electromagnetic spectrum components .......................................................... 9
Figure 2.4: Platforms for remote sensors .......................................................................... 9
Figure 2.5: Type of satellites sensors .............................................................................. 10
Figure 2.6: Spatial resolution .......................................................................................... 11
Figure 2.7: Digital image pixels ..................................................................................... 13
Figure 2.8: Resampling type of images ......................................................................... 14
Figure 2.9: Concept of classification of remotely rensed data ....................................... 16
Figure 2.10: Steps in supervised classification .............................................................. 17
Figure 2.11: Unsupervised classification flow diagram ................................................. 18
Figure 2.12: Methodology of pixel-based and object based processing ........................ 21
Figure 2.13: Optimal thresholding .................................................................................. 24
Figure 2.14: Comparison between edge detection at different thresholding levels ........ 25
Figure 2.15: Concept of object-based feature extraction ................................................ 26
Figure 2.16: Concept of pixel-based feature extraction .................................................. 27
Figure 2.17: Type of step edges ...................................................................................... 28
Figure 2.18: Steps of edge extraction using the Canny operator .................................... 29
Figure 2.19: Applying Gaussian averaging .................................................................... 30
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Figure 2.20: Illustration of gradient calculation in Canny operator ............................... 31
Figure 2.21: Semicircle edge orientation ........................................................................ 33
Figure 2.22: Non maximal suppression procedure ......................................................... 34
Figure 3.1: Combining erosion and dilation to produce an opening or a closing ........... 42
Figure 3.2: Illustration of parameters of hough transform .............................................. 43
Figure 3.3: Template matching technique ...................................................................... 45
Figure 3.4: An LSB-snake of a road segment ................................................................. 48
Figure 3.5: Type of edge detection algorthiems ............................................................. 49
Figure 4.1: Study Area (1) .............................................................................................. 53
Figure 4.2: Study Area (2) .............................................................................................. 53
Figure 4.3: Study Area (3) .............................................................................................. 54
Figure 4.4: Geo-referencing method & toolbar in ARCGIS 10.1 .................................. 55
Figure 4.5: Noise reduction of Study Area (2) ............................................................... 56
Figure 4.6: Histogram of study areas .............................................................................. 56
Figure 4.7: Feature extraction methods and programs ................................................... 57
Figure 4.8: Create feature class in personal geodatabase using Arc-catalge .................. 58
Figure 4.9: Editor toolbar in Arc-map ............................................................................ 58
Figure 4.10: Manual tracking and digitizing edges of the buildings .............................. 59
Figure 4.11: Manual feature extraction of study areas ................................................... 59
Figure 4.12: Feature extraction workflow of ENVI 5.0 ................................................. 61
Figure 4.13: Object based feature extraction toolbox ..................................................... 62
Figure 4.14: Image segmentation result at different levels ............................................. 62
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Figure 4.15: Merging segments result at different levels ............................................... 63
Figure 4.16: Optimal segmentation level 82 and merge level 95 ................................... 63
Figure 4.17: Classfication method and building extraction ............................................ 64
Figure 4.18: Erdas objective process work flow diagram .............................................. 65
Figure 4.19: Configurtions tree view menu of imagine objective ................................. 66
Figure 4.20: Training areas of imagine objective ........................................................... 67
Figure 4.21: Comparison between edge detection at different thresholding levels ........ 68
Figure 4.22: Steps of feature extarction using Erdas ojective 2013 ............................... 70
Figure 4.23: Extract features using Barista program ...................................................... 71
Figure 4.24: Feature extraction dialog of Barista program ............................................ 72
Figure 4.25: Feature Extraction using Canny Operator .................................................. 73
Figure 4.26: Surveying layout of Tayba building ........................................................... 73
1
1 CHAPTER 1: INTRODUCTION
1.1 Scope
This chapter is intended to give a brief overview of research problem emphasizing the
importance of automatic extraction features from imagery to minimizing cost and time
of data acquisition and update GIS database. It gives a description of the research
importance, scope, objectives, methodology, deliverables, as well as thesis organization.
1.2 Background
Humans have sought to extract information from imagery ever since the first
photographic images were acquired. As early as the mid nineteenth century, the French
Army Corps of Engineers experimented with using aerial photographs for
reconnaissance and mapping (Wolf and Dewitt, 2000).
Features extraction from raster images is very important of many GIS activities such as
GIS updating, geo-referencing, and geo-spatial data integration. This process depends
heavily on human labor, which makes GIS database development an expensive and
time-consuming operation when it is performed manually. Automated feature extraction
can significantly reduce the time, cost of data acquisition, update, database development
and turnaround time. Therefore, automated features extraction has been a hot research
topic over the past two decades.
Satellite images contain very rich information and when fused with vector map can
provide a comprehensive view of a geographical area. Google, Yahoo, and Virtual Earth
maps are good examples to show the power of such high resolution images. However,
high resolution images pose great challenges for automatic feature extraction due to the
inherent complexities. First, a typical aerial photo captures everything in the area such
as buildings, cars, trees, etc. Second, different objects are not isolated, but mixed and
interfere with each other, e.g., the shadows of trees on the road, building tops with
similar materials.
2
In addition, the light and weather conditions have big impact over images. Therefore, it
is impossible to predict what and where objects are, and how they look like in a raster
image. All these uncertainties and complexities make the automatic extraction very
difficult.
1.3 Problem Statement
After increase in available imagery in recent years, it followed the launch of several
airborne and satellite photography which are necessary for engineering, planning and
other science fields, it's efficiently dealing with the vast amount of available data
necessitates an increase in automation, while still taking advantage of the skills of a
human operator, some problems were appeared in manual feature extraction from these
images such as accuracy and speed requirements. But, the automated feature extraction
of aerial and satellite images are still at the stage of fundamental research in institutes
and universities in developed countries.
Ten years ago in Gaza Strip, some of municipalities and institutions have begun using
geographic information system (GIS) in projects. Therefore, it was become necessary to
deal with available data from aerial images and extract features to be used in analysis,
planning and decision-making. Nowadays in Palestine, all methods which used in
feature extraction are still traditional (manual methods).
1.4 Research Aim and Objectives
This research aims to investigate and evaluate the current methods that enable as to
extract features from aerial or satellite images automatically.
To achieve this aim, the following objectives are to be determined:
a) Reviewing the available method that can be used to extract specific features such
as (polylines, polygons, and points) from images.
b) Performing an objective evaluation of such methods.
c) Exploring the future developments that is needed to enhance extracting.
d) Showing the integration between GIS and extracted feature database of images.
3
1.5 Methodology
The methodology stages that can be used to achieve the study aim are outlined through
the following lines (see Figure 1.1):
- Literature review on currently available techniques for feature extraction from satellite
images such as (Mathematical Morphology, Hough Transform, Multi-Resolution
Techniques, and Template matching, etc) and assessing these feature extraction
techniques.
- Data collection of aerial photos and satellite images from number of sources that
provide these images (Landsat, IKONOS, Spot, QuickBird satellites).
- Study some of characteristics of aerial photos and satellite images such as (spatial
resolution, spectral bands, types, image surface area ,etc).
- Use existing algorithms to extract features from images using environment
programming.
- Comparison between traditional and automatic methods depending on a set of criteria
to determine the accuracy and success percentage of theses methods.
- Concluding remarks and recommendations.
Figure 1.1: Methodology flowchart
Problem Identification
Literature Review
Data Collection
Data Preparation
Data Extraction Manual Automated
Data Analysis & Evaluation
Conclusion &
Recommendations
4
1.6 Research Structure
This research is oriented into five chapters.
Chapter 1 is intended to give a brief overview of research problem emphasizing the
importance of automatic extraction features from imagery to minimizing cost and time
of data acquisition and update GIS database. It gives a description of the research
importance, scope, objectives, methodology, deliverables, as well as thesis organization.
Chapter 2 describes historic overview of remote sensing technology and its
development stages. It also illustrates the basic of remote sensing and how it works with
more details about each of these elements. This chapter also discusses the types and
characteristics of satellite sensors as well as the most of the common image processing
available in image analysis systems.
Chapter 3 gives a background of historical stages for feature extraction at the beginning
of imagery were acquired by humans, and provides an overview of the types of imagery
being used for feature extraction. This chapter also describes the methods and
techniques that used for feature extraction the quantitative and qualitative accuracy
assessment of these procedures.
Chapter 4 contains detailed description of the steps of the methodology of the research.
It includes steps strategy beginning from data collection of imagery and image
processing of these image to use an existing of automatic feature extraction methods
and methods evaluation.
Finally, Chapter 5 summarizes the conclusion outcomes and outlines the significant
recommendations.
5
2 CHAPTER 2: REMOTE SENSING & DIGITAL IMAGE
PROCESSING
2.1 Scope
This chapter describes historic overview of remote sensing technology and its
development stages. It also illustrates the basic of remote sensing and how it works with
more details about each of these elements. This chapter discusses the types and
characteristics of satellite sensors as well as the most common available image
processing techniques used in image analysis systems.
2.2 Remote Sensing
Remote sensing is the acquisition of information about an object or phenomenon
without making physical contact with the object. In modern usage, the term generally
refers to the use of aerial sensor technologies to detect and classify objects on Earth
(both on the surface, in the atmosphere and oceans) by means of propagated
signals (e.g. electromagnetic radiation emitted from aircraft or satellites) (Aggarwal,
2003).
2.2.1 Historic Overview
Gaspard Tournachon took a slanting photograph of a small village near Paris from a
balloon in 1859. After this picture, the time of remote sensing and earth observation
had started. Other people all over the world soon followed by his example. During the
Civil War in the United States, aerial photography from balloons played an important
role to reveal the defence positions in Virginia (Colwell, 1983).
The next period of development was in Europe not in the United States. Aeroplanes
were used a large scale for photoreconnaissance during World War I. With fast
development of airborne military industries, aircraft proved to be more stable and more
reliable platforms for earth observation than balloons. In the period between World War
I and World War II, a start was made with the civilian use of aerial photos. Application
fields of airborne photos included agriculture, geology, forestry, and cartography. After
these fast developments in technology and industry, application lead to much improved
cameras, films and interpretation equipment. During World War II, most important
6
developments of aerial photography and photo interpretation was accrued. During this
time span, the development of other imaging systems such as near-infrared
photography; thermal sensing and radar took place. Thermal-infrared and Near-infrared
photography proved very valuable to separate real vegetation from disguise. Military
purposes not for civilian purposes was used the first successful airborne imaging radar
but proved valuable for darkness bombing. As such the system was called by the
military ‘plan position indicator’ and was developed in Great Britain in 1941
(Aggarwal, 2003).
Remote sensing systems continued to grow from the systems developed after the wars
in the 1950s. Colour infrared (CIR) photography was discovered to be of great use for
the plant sciences. In 1956, Colwell conducted experiments for the recognition and
classification of vegetation types and the detection of damaged and diseased or stressed
vegetation using of CIR. From 1950s up to now the significant progress in radar and
sensing technology was achieved (Aggarwal, 2003).
2.2.2 Principles of Remote Sensing
Remote sensing, also called earth observation, is the science (and to some extent, art)
can be broadly defined as any process whereby information is gathered about an object,
area or phenomenon without being in contact with it .This is done by sensing and
recording reflected or emitted energy and processing, analyzing, and applying that
information. Our eyes are an excellent example of a remote sensing device. We are able
to gather information about our surroundings by gauging the amount and nature of the
reflectance of visible light energy from some external source (such as nature light as the
sun or industry light bulb) as it reflects off objects in our field of view (Sanderson,
2001).
The process of remote sensing involves an interaction between incident radiation and
the targets of interest. This is exemplified by the use of imaging systems where the
following seven elements are involved. Note, however that remote sensing also involves
the sensing of emitted energy and the use of non-imaging sensors. Figure 2.1 shows the
essential elements of a remote sensing system which included the following lines
(Sanderson, 2001):
7
Figure 2.1: Elements of remote sensing system
1. Energy Source or Illumination (A) - energy source which illuminates or
provides electromagnetic energy to the target of interest consider the first
requirement of remote sensing.
2. Radiation and the Atmosphere (B) - as the energy travels from its source to the
target, it will come in contact with and interact with the atmosphere it passes
through. This interaction may take place a second time as the energy travels
from the target to the sensor.
3. Interaction with the Target (C) – after energy pass through atmosphere and
reach the target; it interacts with the target depending on the properties of both
the target and the radiation.
4. Recording of Energy by the Sensor (D) -we require a sensor (remotely) to
collect and record the electromagnetic radiation after the energy has been
scattered by, or emitted from the target.
5. Transmission, Reception, and Processing (E) - the energy recorded by the
sensor has to be transmitted, often in electronic form, to a receiving and
processing station where the data are processed into an image (hardcopy and/or
digital).
6. Interpretation and Analysis (F) - the processed image is interpreted, visually
and/or digitally or electronically, to extract information about the target which
was illuminated.
8
7. Application (G) – after analysing the raw information from images, the benefits
achieved when we apply the information to better understand of issues and
solving a particular problem in many fields.
2.2.3 Electromagnetic Radiation
Electromagnetic radiation consists of an electrical field which varies in magnitude, in a
direction perpendicular to the direction in which the radiation is traveling, and a
magnetic field oriented at right angles to the electrical field. Both these fields travel at
the speed of light (c) as shown in Figure 2.2 (Sanderson, 2001).
Figure 2.2: Electromagnetic radiation components
2.2.4 42BElectromagnetic Spectrum
The electromagnetic Spectrum is defined as ranges from the shorter wavelengths
(including gamma and x-rays) to the longer wavelengths (including microwaves and
broadcast radio waves) between this ranges our eyes detect visible spectrum, which
consist of three main colors (RGB) (Red – Green – Blue) from wavelengths
approximately 0.4 to 0.7 μm. Moreover there are several regions of the electromagnetic
spectrum which are useful for some remote sensing applications as shown in Figure 2.3
(Aggarwal, 2003).
9
Figure 2.3: Electromagnetic spectrum components
2.2.5 Sensors and Platforms
A sensor is a device that collect, measures and records energy reflected or emitted from
a target or surface (electromagnetic energy). Platforms for remote sensors may be
situated on the ground, on an aircraft or balloon (or some other platform within the
Earth's atmosphere), or on a spacecraft or satellite outside of the Earth's atmosphere
which is considered the famous platforms as shown in Figure 2.4 (Aggarwal, 2003).
Figure 2.4: Platforms for remote sensors
2.2.5.1 Type of Satellite Sensors
Sensors can be divided into two types depending on energy resource. Type (1): passive
sensors depend on an external source of energy, usually the sun. Photographic camera is
considered as passive sensor. Type (2): active sensors have their own source of energy;
an example would be a radar gun. These sensors send out a signal wave and measure the
amount reflected back as shown in Figure 2.5 (Sanderson, 2001).
10
Passive sensors Active sensors
Figure 2.5: Type of satellites sensors
2.2.5.2 Satellite Sensor Characteristics
The principle of most satellite sensors is to gather information about the reflected
radiation along a pathway, also known as the field of view (FOV), as the satellite orbits
the Earth. The smallest area of ground that is sampled is called the instantaneous field
of view (IFOV). The distance between the target being imaged and the sensor on
platform, plays a great role in determining the detail of information obtained and the
total area ground imaged by the sensor.
The data collected by each satellite sensor can be described in terms of spatial, spectral,
radiometric and temporal resolution (Sanderson, 2001).
- Spatial Resolution: The spatial resolution (known as ground resolution) refers to
the size of the smallest possible feature that can be detected on ground by
sensors, which depends primarily on their Instantaneous Field of View
(IFOV).For example the spatial resolution or (IFOV) of Landsat Thematic
Mapper ™ sensor is 30 m.
So, the spatial resolution depends on image applications, some of satellites
collect data at less than one meter spatial resolution but these are classified
military satellites or very expensive commercial systems such as (IKONOS and
OUIKBIRD satellites), Figure 2.6 shows an example at various spatial
resolution (30, 5, 1) meter .
11
Figure 2.6: Spatial resolution
- Spectral Resolution: defined as the number and width of spectral bands in the
sensing device, also describes the ability of a sensor to define fine wavelength
intervals. The simplest form of spectral resolution with one band.
- Radiometric Resolution: The radiometric resolution of an imaging system
describes its ability to discriminate very slight differences in energy. The
radiometric characteristics describe the actual information content in an image.
- Temporal Resolution: Temporal resolution is very important in remote sensing
system which refers to the length of time it takes for a satellite to complete one
entire orbit cycle. The actual temporal resolution of a sensor depends on a
variety of factors, including the satellite/sensor capabilities, the swath overlap,
and latitude. With temporal resolution we are able to monitor changes that take
place on the Earth's surface such as (urban development, floods, oil slicks, etc.)
(Sanderson, 2001). Landsat 5 takes 16 day to complete one entire orbit cycle,
Table 2.1 shows characteristics of various optical remote sensing systems.
12
(Song ,2001) illustrates more details about various optical remote sensing
systems. Table 2.1: Characteristics of various optical remote sensing systems
2.3 22BDigital Image Processing
Today's with high advanced technology most remote sensing data are recorded and
saved in digital format. Digital image processing may involve several procedures
including formatting and correcting of the images data, digital enhancement to facilitate
better visual interpretation, or even automated classification of targets and features
entirely by computer. A digital image that contains graphical information instead of text
or a program. Pixels or cells are the basic building blocks of all digital images. Pixels
13
are small adjoining squares in a matrix across the length and width of your digital image
as shown in Figure 2.7 (Sanderson, 2001).
Each cell contain a digital number (DN) this value of each cell is related to the
brightness, color or reflectance at that point.
Figure 2.7: Digital image pixels
Most of the common image processing functions available in image analysis systems
can be categorized into the following four categories:
• Preprocessing
• Image Enhancement
• Image Transformation
• Image Classification and Analysis
2.3.1 Pre-Processing
Pre-processing includes data operations which normally precede further manipulation
and analysis of the image data to extract specific information. These operations,
sometimes referred to as image restoration and rectification, are intended to correct for
sensor- and platform-specific radiometric and geometric distortions of data. Pre-
processing functions are generally grouped as radiometric or geometric corrections
(Sanderson, 2001).
Radiometric corrections include correcting the data for sensor irregularities and
undesirable sensor or atmospheric noise, and converting the data so they accurately
represent the reflected or emitted radiation measured by the sensor.
14
Geometric corrections include correcting for geometric distortions due to sensor-Earth
geometry variations, and conversion of the data to real world coordinates (e.g. latitude
and longitude) on the Earth's surface. Conversion data to real world coordinates are
carried by analyzing well distributed Ground Control Points (GCPs). This is done in
two steps:
- Geo-referencing: Georeference something means to define its existence
in physical space. That is establishing its location in terms of map
projections or coordinate systems. The term is used both when establishing the
relation between raster or vector images and coordinates.(Linda , 2006)
This involves the calculation of the appropriate transformation from image to
terrain coordinates using GIS software's.
- Geocoding: This step involves resembling the image to obtain a new image in
which all pixels are correctly positioned within the terrain coordinate system.
which defined as mathematical technique used to create a new version of the
image with a different width and/or height in pixels. Increasing the size of an
image is called up sampling; reducing its size is called down sampling. There
are three common resampling methods such as nearest neighborhood, bilinear
interpolation and cubic convolution as shown in Figure 2.8.(Sachs, 2001)
a) Nearest neighborhood : This method is very simple where it assigns the value of
the nearest pixel to the new pixel location.
b) Bilinear interpolation: Assigns the average value of the 4 nearest pixels to the
new pixel location.
c) Cubic convolution: Assigns the average value of the 16 nearest pixels to the new
pixel location.
Figure 2.8: Resampling type of images
15
2.3.2 Image Enhancement
Image enhancement is the modification of an image to make it easier for visual
interpretation and understanding of imagery. The advantage of digital imagery is that it
allows us to manipulate the digital pixel values in an image. Most enhancement
operations distort the original digital values. Image enhancement methods are
(Murayama, 2010):
- Contrast enhancement
In raw imagery, the useful data often populates only a small portion of the
available range of digital values (commonly 8 bits or 256 levels). Contrast
enhancement involves changing the original values so that more of the available
range is used, thereby increasing the contrast between targets and their
backgrounds. Linear contrast stretch is considered the simplest type of contrast
enhancement.
- Density slicing
Density slicing is an enhancement technique whereby the digital numbers
distributed along the x axis of an image histogram are divided into a series of
analyst specified intervals or slices.
- Frequency filtering
Spatial frequency is related to the concept of image texture Spatial filters are
designed to highlight or suppress specific features in an image based on their
spatial frequency.
- Band rationing (Spectral)
Image division or spectral rationing is one of the most common transforms
applied to image data. Image rationing serves to highlight subtle variations in the
spectral responses of various surface covers.
2.3.3 Image Transformation
Digital Image Processing offers a limitless range of possible transformations on
remotely sensed data. Image transformations typically involve the manipulation of
multiple bands of data, whether from a single multispectral image or from two or more
16
images of the same area acquired at different times (i.e. multitemporal image data)
Basic image transformations apply simple arithmetic operations to the image data.
Image transformation methods can be classified in two ways, first theoretical
transformation methods which used some of calculations such as addition and
subtraction, multiplication and division and the application of certain mathematical
models. Second empirical transformation methods such as conversion principal
components also conversion Gradient color and radiation (Sanderson, 2001).
2.3.4 Image Classification and Analysis
Classification of remotely sensed image is used to specify corresponding levels which
called class or "themes" with respect to groups with homogeneous characteristic, with
the aim of discriminating multiple objects from each other within image. The main
objective of image classification is to identify and describe, as a unique gray level (or
color), the features occurring in an image in terms of the object or type of land cover
these features actually represent on the ground (Lillesand and Kiefer, 1994).
Digital image classification uses the spectral information represented by the digital
numbers in one or more spectral bands, and attempts to classify each individual pixel
based on this spectral information as shown in Figure 2.9, but analyst by human
attempting to classify features in an image uses the elements of visual interpretation to
identify homogeneous groups of pixels which represent various features or land cover
classes of interest(Lillesand and Kiefer, 1994).
Figure 2.9: Concept of classification of remotely rensed data
17
Main classification methods are supervised classification and unsupervised
classification.
2.3.4.1 Supervised Classification
The analyst identifies in the imagery homogeneous representative samples of the
different surface cover types (information classes) of interest. In this method, human
identify examples of the information classes (i.e., land cover type) of interest in the
image, these samples are referred to as "training sites" as shown in Figure 2.10 . The
image processing software system is then used to develop a statistical characterization
of the reflectance for each information class. This stage is often called "signature
analysis" and may involve developing a characterization as simple as the mean or the
range of reflectance on each bands, or as complex as detailed analyses of the mean,
variances and covariance over all bands. (Eastman, 1995).
Thus, in a supervised classification operator are first identifying the information classes
which are then used to determine the spectral classes which represent them.
Figure 2.10: Steps in supervised classification
18
2.3.4.2 Unsupervised Classification
Unsupervised classification in essence reverses the supervised classification process.
Spectral classes are grouped first, based solely on the numerical information in the data,
and are then matched by the analyst to information classes (if possible) and does not
require analyst-specified training data. The algorithm used in programs,
called clustering algorithms, are used to determine the natural (statistical) groupings or
structures in the data as shown in Figure 2.11 (Eastman, 1995).
Figure 2.11: Unsupervised classification flow diagram
SEPARATE DATA INTO
GROUPS WITH
CLUSTERING
CLASSIFY DATA INTO
GROUPS
ASSIGN NAME TO EACH
GROUP
SATISFACTORY ?
YES
NO
IMAGE
19
2.3.5 Available Feature Extraction Methods
The term ‘feature’ refers to remote sensing scene objects (e.g. vegetation types,
building, roads, etc.) with similar characteristics (whether they are spectral, spatial or
otherwise). Therefore, the main objective of a feature extraction technique is to
accurately retrieve these features. The term “Feature Extraction” can thus be taken to
encompass a very broad range of techniques and processes. The definition can also be
taken to involve manual , semi-automated and automated vector feature digitizing
(ERDAS, 2013).
Many methods are used for the vectorizing process and feature extraction .Digitizing is
one of a way of conversion of information from analogously produced graphical maps
to machine readable vector or raster formats. Manual and automated methods are
adopted in this study to extract feature from imagery (Stanley, 2003).
2.3.5.1 Manual Digitizing
The simplest way to create vectors from raster layers is to digitize vector objects
manually straight off a computer screen using a mouse or digitizing cursor.
There are two methods of manual digitizing point mode and Stream mode. Both
methods involve the operator moving the cursor on features to be collected. The
difference in the two modes lies in the procedure of collecting those features. (Douglas
and Peucker, 1973; Burroughs, 1986) . The operator manually traces all the lines from
his hardcopy map and creates identical digital map on the computer. It is very time
consuming and level of accuracy is also not excellent.
Almost all programs of GIS can be digitized images using editor toolbar which
available drawing tools (line , polygon , point), some of this programs (ArcGIS,
Geomedia, AutoCAD Map 3D, AutoCAD Raster Design, etc.)
20
2.3.5.2 Automatic Digitizing
Raster and vector are the two basic data structures for storing and manipulating images
and graphics data on a computer. All of the major GIS and CAD software packages
available today are primarily based on one of the two structures, either raster based or
vector based, while they have some extended functions to support other data structures.
Automatic digitizing or so called automated raster to vector conversion, traces lines
automatically from the scanned raster image using image processing and pattern
recognition technique. In this technique, computer traces all the lines which results in
high speed and accuracy along with improved quality of images.
Because of the importance of automated feature extraction from raster to vector
process and the difficulties involved, it has been a major research focus during the past
two decades. Only in recent years, automated raster to vector conversion software on
PCs and small computers become familiar, practical and commercially available for
data acquisition applications.(Yecheng, 1996)
Two methods are used to extract feature from images; one of them is traditional
classification methods are all pixel-based and do not utilize the spatial and context
information of an object and its surroundings, which has potential to further enhance
digital image classification.
The second one is object based feature extraction which is a new method that is widely
used recently. Object based image analysis approach is the approach to image analysis
combining spectral information and spatial information, so with object base approach
not only the spectral information in the image will be used as classification information,
the texture and context information in the image will be combined into classification as
well (Flanders et. al, 2003).
This study reviews and compares between the two methods for feature extraction of
case study images. Figure 2.12 shows the methodology used, which include previous
steps in data preprocessing and image classification (using pixel based and object based
feature extraction).
21
Figure 2.12: Methodology of pixel-based and object based processing
Geometric correction Radiometric correction
Pre-processing
Object Base
Classification
Pixel Base
Classification
Supervised
Classification
Computing Attributes
Image Segmentation
Merging Segments
Processing
Image
Canny Edge Operator
Feature extraction
Gaussian Filtering
Edge Detection and
Linking
22
Before applying automatic feature extraction methods, suitable correction for images
should be applied, the second is step using basics image processing operation to obtain
high contrast between objects and background after applying some of statistical
operations which can reduce noise in the image, which is of benefit to the feature
extraction techniques to be considered later. As such, these basic operations are usually
for preprocessing for later feature extraction or to improve display quality. Typical
image enhancement technique include gray scale conversion ,histogram conversion and
color composition, etc (Nixon and Aguado, 2008).
Image enhancement techniques can be divided into two broad categories:
- Spatial domain methods, which operate directly on pixels.
- frequency domain methods, which operate on the Fourier transform of an image.
Unfortunately, there is no general theory for determining what is `good' image
enhancement when it comes to human perception.
- Histogram
Histogram is spatial domain method and identify and determine it of image is very
important, so the intensity histogram shows how individual brightness levels are
occupied in an image; the image contrast is measured by the range of brightness levels.
The histogram plots the number of pixels with a particular brightness level against the
brightness level. The histogram can be evaluated by the operator histogram, in Code
2.1. The operator first initializes the histogram to zero. Then, the operator works by
counting up the number of image points that have an intensity at a particular value
(Nixon and Aguado, 2008).
Code 2.1: Evaluating the histogram
23
- Thresholding
Thresholding is the simplest method of image segmentation. This operator selects
pixels that have a particular value, or are within a specified range. It can be used to find
objects within a picture if their brightness level (or range) is known and its important
element to edge detection of feature from images. The lower the threshold, the more
edges will be detected, and the result will be increasingly susceptible to noise and
detecting edges of irrelevant features in the image. Conversely a high threshold may
miss subtle edges, or result in fragmented edges (Nixon and Aguado, 2008).
There are more advanced techniques, known as optimal thresholding. These usually
seek to select a value for the threshold that separates an object from its background.
This suggests that the object has a different range of intensities to the background, in
order that an appropriate threshold can be chosen as illustrated in Figure 2.13. Otsu’s
method (Otsu, 1979) is one of the most popular techniques of optimal thresholding
(Nixon and Aguado, 2008). The basis is use of the normalized histogram where the
number of points at each level is divided by the total number of points in the image. As
such, this represents a probability distribution for the intensity levels as :
𝑝(𝑙) =𝑁(𝑙)𝑁2 𝐸𝑞. 1
This can be used to compute the zero- and first-order cumulative moments of the
normalized histogram up to the k th level as
ω(k) = �𝑝(𝑙)𝑘
𝑙=1
𝐸𝑞. 2
and
µ(k) = �𝑙 .𝑝(𝑙) 𝐸𝑞. 3𝑘
𝑙=1
The total mean level of the image is given by
24
µ T = � 𝑙 .𝑝(𝑙)𝑁𝑚𝑎𝑥
𝑙=1 Eq.4
The variance of the class separability is then the ratio
σ𝐵 2 (𝑘) =
�𝜇𝑇.𝜔(𝑘) − 𝜇(𝑘)�2
𝜔(𝑘)�1 − 𝜔(𝑘)� ∀𝑘 ∈ 1,𝑁𝑚𝑎𝑥 𝐸𝑞. 5
The optimal threshold is the level for which the variance of class separability is at its
maximum, namely the optimal threshold Topt is that for which the variance
σ𝐵 2 �𝑇𝑜𝑝𝑡� = 𝑚𝑎𝑥1≤𝐾≤𝑁𝑚𝑎𝑥(σ𝐵
2 (𝐾)) Eq.6
Figure 2.13: Optimal thresholding
The code implementing Otsu’s technique is given in Code 2.2, which followed by
Figure 2.14 which describes the effect optimal, higher and lower value of thersolding on
edge detection (Nixon and Aguado, 2008).
25
Code 2.2: Optimal thresholding by Otsu’s technique
Figure 2.14: Comparison between edge detection at different thresholding levels
26
2.3.5.2.1 Object-Based Feature Extraction
The object based classification concept is that important semantic information necessary
to interpret an image which not represented in single pixels, but in meaningful image
objects and their mutual relations as shown in Figure 2.15. Image analysis is based on
contiguous, homogeneous image regions that are generated by initial image
segmentation. Connecting all the regions, the image content is represented as a network
of image objects. These image objects act as the building blocks for the subsequent
image analysis (Yoon et. al, 2004).
Figure 2.15: Concept of object-based feature extraction
The workflow of object based feature extraction involves the following steps:
- Dividing an image into segments.
- Computing various attributes for the segments.
- Creating several new classes.
- Interactively assigning segments (called training samples) to each class.
- Classifying the entire image with a K Nearest Neighbor (KNN), Support Vector
Machine (SVM), or Principal Components Analysis (PCA) supervised
classification method, based on your training samples.
- Exporting the classes to a shapefile or classification image.
27
2.3.5.2.2 76BPixel-Based Feature Extraction
Traditional remote sensing classification techniques are pixel-based, meaning that
spectral information in each pixel is used to classify imagery as shown in Figure 2.16.
This technique works well with hyperspectral data, but it is not ideal for panchromatic
or multispectral imagery (Yoon et. al, 2004).
Figure 2.16: Concept of pixel-based feature extraction
A conventional pixel-based classification approach, based on statistical algorithms has
been used for decades (Tso and Mather 2009). Generally, this approach is very useful in
large scale images where a separation can be efficiently established between water,
urban, and vegetation areas according to their spectral characteristics. However, in cases
of similar spectral information the ability of this approach is limited (Yan 2003).
The main assumption in using this approach is that the single pixel contains sufficient
grey level information to be assigned to a certain class. The challenge in using this
approach, especially in residential areas many objects such as; buildings, concrete roads,
sidewalks and parking lots will have a nearly identical spectral response as the main
construction material is almost the same. The unsupervised classification approach is
often more suitable in an automatic classification solution, where user interference is
not required. The primary difference between the unsupervised and supervised
approaches is that for the unsupervised methods, only the number of clusters are entered
without selecting any training data set, and the classifier automatically constructs the
clusters by minimizing a predefined error function (Yiu-ming 2005).
28
Many approaches to image interpretation are based on edges, since analysis based on
edge detection is insensitive to change in the overall illumination level, edge detection
highlights image contrast. There are many edge detection methods such as Sobel,
Prewitt, Roberts, Canny. This section discussed canny edge detector (Nixon and
Aguado, 2008).
-Canny edge detector
The Canny edge detection operator (Canny, 1986) is perhaps the most popular edge
detection technique at present. It was formulated with three main objectives:
• optimal detection with no spurious responses.
• good localization with minimal distance between detected and true edge
position.
• single response to eliminate multiple responses to a single edge.
Canny edge detection is an optimal method for step edges’ detection in the spatial
domain. Canny uses three criteria to design his edge detector. First, a reliable detection
of edges with low probability of missing true edges, and a low probability of detecting
false edges must be achieved. Second, the detected edges should have a minimum
distance to the true location along the edge. Third, there should be only one response to
a single edge (thin lines for edges), Figure 2.17 shows the type of step edges (Nixon and
Aguado, 2008).
Figure 2.17: Type of step edges
Depending on these criteria, the Canny edge detector first reduce the response to noises,
this can be effected by optimal smoothing, then it finds the image gradient to highlight
regions with high derivatives. The regions in image with high derivatives are tracked by
the algorithm to suppress any pixel that is not at the maximum (non-maximum
suppression). The remaining pixels are further reduced by two thresholds T1 and T2. If
29
the magnitude is below T1, that’s mean none edge so it is set to zero, if the magnitude is
above T2, it is made an edge. If the magnitude is between the two thresholds, then it is
set to zero unless there is a path from this pixel to a pixel with a gradient above T2
(Canny, 1986).
Image edge extraction using the Canny Operator consists of six steps as shown in
workflow in Figure 2.18.
Figure 2.18: Steps of edge extraction using the Canny operator
Gaussian filtering
Computation of gradients
Edge pixel classification
Non-maxima suppression
Subpixel estimation
Edge tracking and
thinning
Feature extraction
Case Study Image
Can
ny O
pera
tor
30
In order to implement the canny edge detector algorithm, a series of steps must be
followed.
Step 1: Gaussian Filtering
The first step of canny edge detection is to filter image any noise in the original image
before trying to locate and detect any edges. The Gaussian filter is used to blur and
remove unwanted detail and noise.
The Gaussian operator has been considered to be optimal for image smoothing. The
template for the Gaussian operator has values set by the Gaussian relationship. The
Gaussian function g at coordinates x, y is controlled by the variance σ2 according to:
𝑔(𝑥,𝑦,𝜎) = 1
2𝜋𝜎2𝑒−�
𝑥2+𝑦22𝜎2 � 𝐸𝑞. 7
The 3×3 operator (Figure 2.19a) retains many more of the features than those retained
by direct averaging. The effect of larger size is to remove more noise at the expense of
losing small features in images as is clear in 5×5 and the 7×7 operators in Figure 2.19
(b) and (c), respectively (Nixon and Aguado, 2008).
Figure 2.19: Applying Gaussian averaging
31
Step 2: Gradient calculation
After smoothing the image and eliminating the noise, the next step is to find the edge
strength by taking the gradient of the image –there are many ways and masks to perform
the gradient calculation such as simple derivative, derivative of Gaussian and Slobel
operator. Canny algorithm basically finds edges where the gray scale intensity of the
image changes the most. These areas are found by determining gradients of the image.
Gradients at each pixel in the smoothed image are determined by applying what is
known as the Sobel-operator.
When finding edges, looking for the steepest descent as well as the steepest ascent since
both represent a high change in the intensity of the image. Figure 2.20 depicts the
gradient and orientation process (Rangarajan, 2002).
Figure 2.20: Illustration of gradient calculation in Canny operator
The gradient value for each pixel we can get the magnitude of the gradient by:
�∇𝐼𝑥,𝑦� = �(𝑑𝑥𝑥,𝑦 )2 + (𝑑𝑦𝑥,𝑦 )2 𝐸𝑞. 8
The main purpose of doing this is to highlight regions with high spatial derivatives. The
orientation of the edge can be determined by the next equation:
𝜃 = arctan �𝑑𝑦𝑥,𝑦
𝑑𝑥𝑥,𝑦� Eq.9
32
The Sobel function (Code 2.3) convolves the generalized Sobel template with the
picture supplied as argument, to give outputs which are the images of edge magnitude
and direction, in vector form (Nixon and Aguado, 2008).
Code 2.3: Generalized Sobel operator
Step 3: Edge pixel classification
The lengths of the gradient vector computed previously are compared to a threshold.
Pixels with a gradient vector length smaller than that threshold are classified as
homogeneous pixels, whereas pixels with a gradient vector length larger than that
threshold are classified as edge pixels.
Once the edge classified and direction is known from orientation equation, the next step
is to relate the edge direction to a direction that can be traced in an image. So if the
pixels of a 5x5 image are aligned as follows:
x x x x x x x x x x x x a x x x x x x x x x x x x
Then, it can be seen by looking at pixel "a", there are only four possible directions when
describing the surrounding pixels ,in the horizontal direction (0 degrees) , along the
positive diagonal (45 degrees) , in the vertical direction (90 degrees), or 135 degrees
33
(along the negative diagonal). So the edge orientation has to be resolved into one of
these four directions depending on which direction it is closest to (e.g. if the orientation
angle is found to be 3 degrees, make it zero degrees). Think of this as taking a
semicircle and dividing it into 5 regions as shown in Figure 2.21 (Rangarajan, 2002).
Figure 2.21: Semicircle edge orientation
Therefore, any edge direction falling within the yellow range (0 to 22.5 & 157.5 to 180
degrees) is set to 0 degrees. Any edge direction falling in the green range (22.5 to 67.5
degrees) is set to 45 degrees. Any edge direction falling in the blue range (67.5 to 112.5
degrees) is set to 90 degrees. And finally, any edge direction falling within the red range
(112.5 to 157.5 degrees) is set to 135 degrees (Rangarajan, 2002).
Step 4: Non-Maximal Suppression
This step works with the magnitude and orientation of the gradient of the pixel under
consideration and creates one pixel-width edge and locates the highest points in the
edge magnitude data. For all pixels classified as edge pixels, their gradient vector
lengths are compared to the lengths of the two neighboring edge pixels in gradient
direction. If any of these two neighbours has a larger gradient length, the current pixel is
not a local maximum of gradient vector length, and thus the pixel is declared not to be
an edge pixel. Thus, only the most significant edge pixels remain as shown in Figure
2.22 (Rangarajan, 2002).
34
Figure 2.22: Non maximal suppression procedure
The non-maximum suppression operator, non_max in Code 2.4
Code 2.4: Non-maximum suppression
35
Step 5: Subpixel estimation
For the remaining edge pixels, a subpixel position of the actual edge point is carried out.
The gradients in gradient direction are approximated by a second order polynomial, and
the position of the edge point is estimated as the position of the maximum of the
polynomial.
Step 6: Edge tracking
Neighbouring edge pixels are connected by an edge tracking algorithm, which results in
edge pixel chains. These edge pixel chains will be split at junctions (i.e. where edges
intersect) and at points of high curvature. After that, short edge pixel chains will be
discarded. The Minimum Line Length in [pixels] can be selected in the according
numerical field. Each edge pixel chain is approximated by a polygon in an iterative
procedure: Splitting, Approximation and Merging which defined as edge thinning
(Rangarajan, 2002).
2.4 Conclusion
After reviewing the pervious subjects of remote sensing and image processing to extract
feature from imagery using available feature extraction methods such as manual and
automatic, it is clear that should be use image pre-processing before extract feature from
imagery to enhance resolution. Pixel based feature extraction based on single pixel and
use statistical algorithms but Object based feature extraction based on contiguous,
homogeneous image regions.
36
3 CHAPTER 3: LITERATURE REVIEW
3.1 Scope
This chapter gives a background of historical stages for feature extraction at the
beginning of imagery were acquired by humans, and provides an overview of the types
of imagery being used for feature extraction. This chapter also describes the methods
and techniques used for feature extraction and the quantitative and qualitative accuracy
assessment of these procedures..
3.2 Background
Since the first photographic images were acquired the humans have sought to extract
information and data from imagery. As early as the mid nineteenth century the French
Army Corps of Engineers experimented with using aerial photographs for
reconnaissance and mapping (Wolf and Dewitt, 2000). With rapid development in
technology the expansion and improvement of photogrammetry and remote sensing
techniques stimulated by advances such as the development of color film, the invention
of the airplane, and unceasing improvement in instrumentation and techniques (Wolf
and Dewitt, 2000).
On the other hand, interest in feature extraction field has increased significantly since
the advent of digital imagery and the possibilities associated with electronic processing.
Many of papers and researches with focused conferences provide an overview of many
of the techniques available in theses filed (Baltsavias, et al., 2001; Gruen, et al., 1997;
Gruen, et al., 1995). Other companies such as CRCSI (2011), Erdas Imagine
(Intergraph) (2013), Definiens (2003) and Visual Learning Systems (VLS, 2003) and
others are developing software specifically targeted at feature extraction with various
accuracy and speed .
Technology has improved and commercial access to imagery has continued to expand
and restriction to get of these images became low, so the research into automated
feature extraction from imagery increased . The first panchromatic imagery following
the launch of the first SPOT satellite in 1986 used from Destival (1986) to described the
improvements in feature extraction that were expected using 10 meter resolution. In
moderate resolution imagery with high sensors, such as SPOT, Geo eye, Landsat
37
Thematic Mapper (TM) and IKONOS, linear features such as roads are often narrower
and more accurate and clear than the spatial resolution of the satellite. Many problems
founded in imagery after captured ,so Hemmer (1996) described the problem in pixel of
images as one of the confusing factors in extracting features using imagery from the
satellite sensors available at that time. Wang and Zhang (2000) compared SPOT and
Landsat TM imagery with high spatial resolution aerial photography for extracting road
networks and found that the success of linear feature extraction with more accurate was
particularly related to spatial resolution of images. Their experimentation found that
photography out-performed the lower resolution satellite imagery when extracting roads
in an urban environment. High spatial resolution imagery provides more details
representation of features such as building and road networks and others elements on
maps which needed for many applications that cannot be obtained from lower resolution
image sources (Xiong, 2001). Roads networks appear as curvilinear structures and
building appear as overlapping areas in lower resolution imagery, while in higher
resolution imagery roads and building appear as homogenous regions that satisfy certain
shape or size constraints (Hinz, et al., 2001).
On the other hand, many of the techniques developed for line feature detection search
for roads networks in high spatial resolution imagery as pairs of edges: such techniques
are unsuitable to processing lower resolution imagery.
A common objective of feature extraction is to facilitate the rapid update of GIS data.
(Bonnefon, et al., 2002). Manual digitizing method of data depends on heavily of
human labor, pushing up the cost of developing such databases (Xiong, 2001). An
important factor in developing feature extraction methods and techniques is reduction
the time in creating and updating databases. The Canada Centre for Mapping (CCM)
traditionally updated maps through visual interpretation of imagery (Manual method)
(O’Brien, 1989). The Digitization of maps at the CCM led to a logical move from
manual updates to extraction of information directly from digital sources. Improving the
quality ,consistency and increase of speed derived data from imagery may be a further
reason for using and developing automated procedures.
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3.3 Types of Imagery Used in Feature Extraction
Feature extraction techniques are applied to imagery with a wide range of spectral and
spatial characteristics. Many authors have reported use of panchromatic aerial imagery
for extracting linear and polygon features as (e.g., Agouris, et al., 2001a; Katartzis, et
al., 2001; Couloigner and Ranchin, 2000; Trinder and Wang, 1998). Also researcher has
used of radar data for extracting linear features (e.g., Hellwich, et al., 2002; Chanussot,
et al., 1999; Tupin, et al., 1998; Iisaka and Sakurai-Amano, 1995), while others such as
Liang, Shiahn and Ming (2007) extracted feature from lidar data; additionally, authors
such as Pelletier (1985) describe feature extraction in agricultural region using of
thermal imagery. Multispectral image sources in the visible or near-infrared portion of
the spectrum such as SPOT was used from several authors to applied feature extraction
techniques (e.g., Nguyen, 2012 and Hui, 2001), Landsat TM (Nguyen, 2012; Wang and
Zhang, 2000), and IKONOS imagery (Lee and Shan, 2012; Gibson, 2003; Dial, et al.,
2001).
Research has also been performed for linear feature extraction from hyperspectral
imagery. (Gardner, et al., 2001). Penn and Livo (2002) reported some success in
extracting road locations from AVIRIS imagery, while Doucette, et al. (1999)
experimented with HYDICE imagery.
Many of advanced sensors provided high resolution of imagery such as GeoEye-1 has
1.65 meter multispectral and 0.5 meter panchromatic, IKONOS has four meter
multispectral and one meter panchromatic; QuickBird imagery has 2.44 meter
multispectral and 0.61 meter panchromatic. With availability of these images much of
the research reported for feature extraction applies to single band, high spatial
resolution imagery. Ikonos (Space Imaging, Inc., 2003) and Quickbird (Digital Globe,
2003) are examples of the new generation of high spatial resolution satellite based
sensors. These sensors record their highest spatial resolution in a panchromatic mode.
Processing techniques that aim to use the highest spatial resolution data sources will
need to extract and exploit the spatial and/or contextual information with a limited
number of spectral channels (Guindon, 1999).
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3.4 Feature Extraction
3.4.1 Manual Versus Automated Extraction
The simplest methods of feature extraction is manually using visual digitizing of
imagery so humans and computers have complementary strengths: humans are good at
digitizing large areas and recognizing objects which depend on vision optical, whereas
computers are good at optimization of images, detailed delineation and repetition
(McKeown, et al., 1996). Whether automated, manual or a combination of the two
methods, feature extraction can be a very involved process. Manual feature extraction
method needed to investment skills of the operator but can be time consuming and thus
expensive to perform (Baumgartner, et al., 1999). With rapidly changing society and
growing body of digital images archived, the efficient revision of cartographic databases
using GIS implies some form of automated feature extraction (O’Brien, 1989).
Humans by manual digitizing have the ability to digitize and group simple features,
such as points and lines, into meaningful structures . Semi-automated approaches
depend on user provided cues to delineate feature components (Agouris, et al., 2001).
Only a short number of years ago considered fully automatic methods for feature
extraction to be “far out of reach.” (Gruen and Li 1997)
Data collection is often the most expensive component in a GIS application and using
techniques developed that can alleviate this (Ansoult, et al., 1990; Firestone, et al.,
1996). Automated methods offer for consumed time and labor savings and potentially
may improve consistency and accuracy data extracted. Using of automated or semi-
automated methods can also provide cost savings by reducing the training time of photo
interpreters (Pigeon, et al., 1999). Also have other less tangible benefits such as
reducing operator fatigue.
With the availability of high spatial resolution imagery, it is often possible to consider
spatial patterns to a greater degree when looking for specific features on maps. For
example, it is possible to use structural information about roads networks (such as,
linearity, width,…etc.) to distinguish them from other features that may be spectrally
similar. Most of automated or semi-automated feature extraction procedures try to
40
simulate the human interpretation process by incorporating both spectral and spatial
information.
On the other hand, the authors and researchers believe that using a semi-automated in
digitizing imagery approach is optimal method because the ability of humans to
identification objects almost flawlessly with limited effort such as Baumgartner, et al.
(1999) found that their automatic road extraction was not absolutely reliable and
generally required a human operator to edit the results. Humans by optical vision are
able to understand shapes of objects in noisy data and adapt to varying conditions,
without being told explicitly what to expect. The significant challenge is writing
computer code (Algorithms) to simulate this ability.
3.4.2 A Feature Model
Automated feature extraction requires that such a model be defined in a manner that can
be implemented by computer (Trinder and Wang, 1998). Model based processing
exploits the constraints and relationships that define objects, for example, the size,
shape, and material of a building, or the width, material, and direction of a road. The
feature model includes information relating to a range of characteristics such as
intensity, shape, texture, and context (Suetens, et al., 1992). Models are often
characterized as being either flexible or rigid. A rigid model defines features
specifically, for example outlining the allowable size, shape and spectral response. A
flexible model may include specifications in terms of generic constraints, such as
smoothness, rectilinearity, curvature, compactness, symmetry, and homogeneity. An
objective function is used to find a best fit between the model and the image data. Some
techniques use hierarchical types of models (Suetens, et al., 1992). For example, Yee
(1987) identified bridges by first finding potential road segments, then restricting the
search to select those with water on either side.
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3.5 Techniques for Feature Extraction
There are a lot of techniques were used in recent years to detect and extract linear and
polygon features from imagery. Many of the authors and researchers wrote papers to
review developed extraction techniques for locating a specific feature class in imagery,
such as roads, building and agricultural area (Fischler, et al., 1981).
For example the techniques for linear feature extraction from imagery often divide into
three primary steps: edge detection or road finding, road tracking, and vectorization or
road linking (Park, et al., 2002; Trinder and Wang, 1998). While the rectangle
extraction and detection methods are mainly based on grouping of primitives, like edges
or segments or matching the predefined parametric models.( Kailasrao, et al., 2012).
The level of automation feature extraction techniques varied significantly from human
intervention or not. Some of procedures use a few initial assumptions, such as the
relative brightness of road and building pixels or linearity, and allow the computer to do
the rest.
Many of the techniques reported in the literature combine strategies from a variety of
approaches. Categorizing such approaches becomes a challenge. The following sections
present many of the different techniques (Algorithms) used in automatically extracting
feature from imagery, recognizing that there is often substantial overlap between the
procedures.
3.5.1 Mathematical Morphology
The techniques of mathematical morphology (MM) have proven useful in automating
feature extraction. Mathematical morphology is a theory which provides a number of
useful tools for image analysis, so it has been widely used in digital image processing
and focuses on the area that studies the geometric properties of objects in the images.
This allows the extraction of image components that are useful in the representation and
description of the shape of a region, such as borders and skeletons (Gonzales and
Woods, 2000).
Daryal and Kumar (2010) used mathematical morphology to extract lines developed in
the software MATLAB. Frigato (2008) also used mathematical morphology to carry
out the semi-automatic extraction of linear features. Castro and Centeno (2010) used
42
mathematical morphology to extract lines from Advanced Land Observing Satellite
(ALOS) images. Dong (1997) used mathematical morphology to extract linear features
from gray scale aerial imagery.
In mathematical morphology, images are filtered using a kernel. The output of the
filtering process depends on the match between the image and the kernel and the
operation being performed (O’Brien, 1989). The two basic operations of mathematical
morphology are dilation and erosion (Serra, 1986). The simplest example of
mathematical morphology considers the analysis of binary images. The kernel typically
used for binary imagery is a 3 × 3 array consisting of 0s, or1s. Dilation and erosion can
be performed on binary images using with the kernel as shown in Figure 3.1 (Ali,
2010).
Figure 3.1: Combining erosion and dilation to produce an opening or a closing
3.5.2 Hough Transform
Hough transform is an feature extraction technique used in image analysis, computer
vision, and digital image processing. (Shapiro, 2001). It's an automatic analysis
technique used for detection of linear features in a variety of applications (Karnielli, et
al., 1996). The classical Hough transform was concerned with the identification
of lines in the image, but later the Hough transform has been extended to identifying
positions of arbitrary shapes. The HT uses a parametric approach to describe features of
interest and can detect any feature that can be parameterized (Fitton and Cox, 1998). In
the parameter space, image patterns produce local extremes at the most likely parameter
values (Suetens, et al., 1992).
43
Successful detection of linear features using the HT requires preprocessing to threshold
the input image into a binary layer. Benefits of the HT are that it detects lines with some
fragmentation and it is reasonably unaffected by random noise (Fitton and Cox, 1998).
Turker and San (2010) used Hough Transform to extraction building from high
resolution satellite images. Lohani and Singh (2007) used and developed Hough
Transform for extraction building from LiDAR (Light Detection And Ranging Data).
Lee and Moon (2002) used the parameterization of the HT described by Richard
Duda and Peter Hart (1972) for extracting linear features. Stylianidis and patias (2000)
used Hough Transform in line extraction.
Karnielli, et al. (1996) used the HT to detect linear geological features using three
different image sources: digitized terrestrial photography, digitized airborne
photography, and Landsat TM Imagery.
In this transform image space (x, y) is transformed to a (ρ, θ) parameter space. An
example illustrating this parameterization is shown in Figure 3.2. Based on the example
shown in Figure 3.2, the point (x, y) can be represented in polar coordinates as (r, α).
That is: x = r cos α and y = r sin α. The following can also be observed from Figure 3.2:
ρ = r cos β = r cos (θ - α) = r cos θ cos α + r sin θ sin α
= (x/cos α) cos θ cos α + (y/sin α) sin θ sin α = x cos θ + y sin θ Eq.3.1
Figure 3.2: Illustration of parameters of hough transform
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3.5.3 Multi-Resolution Techniques
The appearance of roads in digital imagery is dependent on the spectral and radiometric
characteristics of the sensor and the spatial resolution of the imagery. In lower spatial
resolution imagery, roads tend to appear as lines as compared to higher resolution
imagery (less than two meter GSD) where roads appear as elongated homogeneous
regions with consistent width (Baumgartner, et al., 1999). Many authors (e.g.,Wang, et
al. 2005; Gibson, 2003; Couloigner and Ranchin, 2000; Baumgartner, et al., 1999;
Daida and Vesecky, 1991; Trinder and Wang, 1998; Shneier, 1982) report multi-
resolution based approaches to extract roads from imagery.
Many multi-resolution approaches generate lower resolution imagery by degrading a
high-resolution source. Both Shneier (1982) and Baumgartner, et al. (1999) extracted
lines in imagery with reduced resolution, then used this information to identify the roads
in the higher resolution imagery. The image degradation often involves generating an
image pyramid. Shneier (1982) created a pyramid of images by successively passing a 2
× 2 filter over the image and replacing the four-pixel neighborhood with the median
value. As an alternative, Couloigner and Ranchin (2000) used a wavelet transform to
generate pyramid layers. Instead of degrading a high-resolution dataset for multi-
resolution analysis, some authors use multiple image types. Bonnefon, et al. (2002) used
SPOT imagery to approximately identify linear features then used this preliminary data
to identify roads in IKONOS imagery.
Baumgartner, et al. (1999) used a texture- based segmentation procedure to subdivide
the imagery into three regions (urban, rural, and forest) and developed local road
models to suit each region. In rural areas more than 95 percent of roads extracted were
actually roads and 80 to 90 percent of roads were extracted. This approach was less
successful in urban areas, with a visual assessment showing that the fragmented roads in
the built-up area were a challenge for the automated processing algorithm.
3.5.4 Template Matching
Template matching is a technique in digital image processing for finding small parts of
an image which match a template image.(Brunelli, 2009). In this approach, a template
describing the general characteristics of the feature of interest is defined. Templates are
often fixed in terms of attributes such as size, shape, and intensity. Features are
45
extracted by moving the template through the image and evaluating the match at each
location using a similarity measure (e.g., correlation) to find optimal locations (Suetens,
et al., 1992) and match is done on a pixel-by-pixel basis as shown in Figure 3.3. While
the science behind Feature Analyst® (VLSI, 2003), an extension to ESRI ARCGIS®
and ERDAS IMAGINE® software, remains proprietary, the machine learning approach
implemented utilizes user-defined templates when searching for specific features.
Figure 3.3: Template matching technique
Such templates incorporate both spatial and spectral information. Lina et al; (2008) used
template matching with angular texture signature to extract road from high resolution
imagery. Opitz (2002) evaluated Feature Analyst for extracting roads in pan-sharpened
Ikonos imagery and he found that the automated tool provided accurate results with a
substantial reduction in labor. Rak and Kim (2001) developed semi – automatic road
extraction algorithm from IKONOS images using template matching. Poz (2001)
presented an edge following technique that used a local template to define roads in
imagery with a 2 meter pixel size. Poz (2001) evaluated edges by comparing image
regions to rotated templates of width equal to the road. The technique required seed
points from the operator and relied on a well-defined road model. The road tracing
method was visually determined to be successful in the relatively simple test case
presented by Poz (2001).
3.5.5 Dynamic Programming
The term dynamic programming was originally used in the 1940s by Richard
Bellman to describe the process of solving problems where one needs to find the best
decisions one after another. Dynamic programming is a technique for solving
optimization problems when not all variables in the evaluation function are interrelated
simultaneously (Ballard and Brown, 1982). It is a solution strategy for combined
46
optimization problems which involve a sequential decision-making process. It is an
optimization process, expressed as a recursive search (Bellman and Dreyfus, 1962).
This approach is applicable only if a function can be expressed in terms of relationships
between neighboring pixels alone and involves a sequential decision making process
(Gruen and Li, 1997). Gruen and Li (1997) introduced an application , dynamic
programming was used to optimize line feature extraction such as road in SPOT
imagery based on the procedure summarized below:
• Define a curve as a polygon with n vertices.
• Develop a merit function based on several of the radiometric and geometric properties
of the road model (as described in an earlier section e.g., roads are bright, smooth, linear
features, with little change in intensity over a short distance), with limitations on road
curvature forming a fixed constraint.
• Move each vertex around in a window (for example, 5 × 5) to find the maximum of
the merit function based on the characteristics described above.
Poz and Vale (2003) used dynamic programming for semi-automated road extraction
from medium and high resolution images. Bonnefon, et al. (2002) also applied dynamic
programming to find an optimal solution when using SPOT imagery to update existing
GIS data layers.
3.5.6 Particle Swarm Optimization
Particle swarm optimization (PSO) is a swarm intelligence based algorithm to find a
solution to an optimization problem in a search space, or model and predict social
behavior in the presence of objectives. It is a kind of swarm intelligence that is based on
social-psychological principles and provides insights into social behavior, as well as
contributing to engineering applications. The particle swarm optimization algorithm
was first described in 1995 by James Kennedy and Russell C. Eberhart.
Kundra et al. (2010) developed algorithm which based on global threshold (average)
using PSO to detect and extraction objects from images. Setayesh et al. (2010) gave
new contribution to object detection is application of particle swarm optimization for
extraction of geometric properties of an object in an image for accurate recognition
47
especially in noisy environments the edges and the corners of an object are detected by
particle swarm optimization algorithm and then the object is classified based on number
of the corners and attributes of the edges by a simple fuzzy rule-based classifier. Yang
et al. (2006) used PSO to road extraction from (Synthetic-Aperture Radar) SAR Images.
Firstly, manually select the road’s extremities. Secondly, calculate the each pixel's road
membership value using local road detector in the original SAR images. Thirdly, with
particle swarm optimization that is one of the most powerful methods for optimization
problem we obtain the optimal B-spline control points from the result of road detection.
3.5.7 Pixel Swapping
Iisaka and Sakurai-Amano (1995) describe a feature detection approach combining
spectral and spatial information. In traditional spectral analysis, the intensity of a pixel
(ai) is defined simply by the location of the pixel (xi, yi), that is, spatial relationships
between pixels are generally not considered. Iisaka and Sakurai-Amano (1995)
described a spectral relationship as a mapping between two ordered sets.
3.5.8 LSB Snake
LSB-Snakes derive their name from the fact that they are a combination of least squares
template matching (Grain, 1985) and B-spline Snakes. B-spline Snakes have been
applied to satellite and aerial images as shown in Figure 3.4 (Trinder and Li, 1995; Li,
1997) and are an alternative to the polygonal curves. For LSB-Snakes we use three
types of observations, which are also based on the generic road model. These
observations can be divided in two classes, photometric observations, that represent the
gray level matching of images with the object model, and geometric observations that
express the geometric constraints and the a priori knowledge of the location and shape
of the feature to be extracted. Li and Gruen (1996) used LSB snake to linear feature
extraction from multiple images. Li and Gruen (1995) developed automation of Road
Extraction from Space and Aerial Images using LSB snake method.
A visual evaluation performed by Gruen and Li (1997) showed that the LSB-SNAKES
technique was successful even when faced with varying road width, and partial-
occlusion caused by buildings, trees and cars.
48
Figure 3.4: An LSB-snake of a road segment
3.5.9 Edge Detection
Edge detection is the name for a set of mathematical methods which aim at identifying
points in a digital image at which the image brightness changes sharply or, more
formally, has discontinuities. There are many methods for edge detection, but most of
them can be grouped into two categories, search-based and zero-crossing based.
The search-based methods such as (Sobel, Prewitt, Roberts, Canny) as shown in Figure
3.5 detect edges by first computing a measure of edge strength, usually a first-order
derivative expression such as the gradient magnitude, and then searching for local
directional maxima of the gradient magnitude using a computed estimate of the local
orientation of the edge, usually the gradient direction. The zero-crossing based methods
search for zero crossings in a second-order derivative expression computed from the
image in order to find edges. (Gonzalez, 2002)
By far the most well-known edge extraction method is the Canny edge detector.
Canny’s approach was to determine the “optimal” edge detection method for a certain
set of assumptions. Is in fact an approximation to the optimal detector for a step edge
under Gaussian noise.
The three stated objectives of Canny Edge Detection are:
1. Good Detection: There should be a low probability of both false negatives and false
positives.
2. Good Localization: The detected edge points should be close to the true edge.
(a) Initial position (b) Final solution
49
3. Single Response: In contrast to the Laplacian, each image edge should generate only
a single output edge.
Subash, (2011) extracted road networks automatically from satellite images using
extended kalman filtering and efficient particle filtering and the system used canny edge
detection to find roads edge. San and Turker, (2010) extracted building from high
resolution satellite image using Hough transform and after detecting the building
patches, their edges are detected using the Canny edge detection algorithm. Xiao,
(2007) applied the Canny operator to the panchromatic images for edge detection to
extraction building from very high resolution satellite imagery to generate 3D city
model.
Figure 3.5: Type of edge detection algorthiems
3.6 Knowledge Integration
Knowledge engineering is the task of converting knowledge, which may be intuitive,
into some exploitable form (Pigeon, et al., 1999). Combining rules from a variety of
sources, including human intuition, can be challenging. Some commercial software
vendors now provide tools that support integrating rules from a variety of image and
ancillary data sources, for example Expert Classifier©, a component of ERDAS
IMAGINE® (Leica, 2003). Fischler, et al. (1981) used a knowledge integration
approach to locate roads in imagery. Fischler, et al. (1981) used rules to establish a
numerical score for each pixel to indicate the likelihood of that pixel lying on a road
(low values represented a greater probability). The road location was determined by
finding the lowest cost route that satisfied all imposed constraints, such as continuity.
This process can combine a variety of local image feature operators (such as edge
detectors) and additional constraint layers in order to optimize the search for roads.
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Scores from different layers are combined based on knowledge, for example, about the
responsiveness of the operators used and the applicability to a particular scene.
3.7 Classification-based Feature Extraction
A number of articles specifically take advantage of the multispectral nature of sensors
such as Landsat TM, SPOT and IKONOS to extract road information. Classification is
useful as a preprocessing step in feature extraction, for example, to segment images and
focus a model on particular cover types. Classification techniques have also been
directly applied to solving the problem of linear feature extraction. In most cases, even
with hyperspectral datasets, the spectral information alone was not sufficient to define
roads and the classification was one component of a multistage process. Gardner, et al.
(2001) found that classification of roads using AVIRIS imagery was challenging
because of the similarities of construction materials in roads and roofs. They found that
following the classification with the spatial pattern recognition technique of a Q-tree
filter improved the result. In some applications, a road network is simply one
component of an output product.
3.8 Assessing Feature Extraction Techniques
Most of the papers reviewed relied heavily on visual assessment for reporting the
success of the feature extraction algorithm. For example, Yee (1987) visually compared
road extraction using two different automated methods to roads extracted manually,
reporting only that the manual identification results were comparable to that of the
automated procedure. Baumgartner, et al. (1999) compared roads automatically
identified in several test images with manually plotted reference data to report errors of
omission and commission in applying their road extracting techniques. Baumgartner, et
al. (1999) also reported the relative geometric accuracy for correctly identified roads,
comparing the distance in pixels between visually identified road locations and those
that had been extracted automatically. Such assessments are limited by the accuracy of
the manual road identification. Authors such as Agouris, et al. (2001) experimented
with synthetic images in order to evaluate the validity of their algorithm. For the papers
that did report accuracy statistics, the most commonly reported measures were total
correct, errors of commission and errors of omission. Some authors (e.g., Fischler, et al.,
1981) also defined performance criteria to evaluate their extraction technique.
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Many authors state that automated feature extraction procedures provide significant
benefits in terms of saving operator time and effort. In spite of this, few of the studies
reviewed stated results to support this assertion.
3.9 Conclusion
Many authors list a host of reasons for automating feature extraction, ranging from time,
cost and energy savings, to product improvements, such as, increased detail or accuracy.
Unfortunately, many of the papers reviewed did not provide results to support these
claims. Many authors stated intentions of locating points in the field for verification of
absolute position, but did not include accuracy statistics for the study reported.
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4 CHAPTER 4: METHODS EVALUATION
4.1 Scope
This chapter contains detailed description of the steps of the methodology of the
research. It includes steps strategy beginning from data collection of imagery and
image processing of these image to using an existing of automatic feature extraction
methods and methods evaluation.
4.2 Data Collection and Preparation
Maps have been the main source of data for geographic analysis for many years. Raster
data is commonly obtained by scanning maps or collecting aerial photographs and
satellite images.
4.2.1 Data Collection
There are a lot of free sources that offer free aerial photos and satellite images in the
internet as USGS (U.S. Geological Survey) and NASA website but these images with
low resolution in conflict zones as Palestine and from other sources as Gaza
municipality. After many of trials to get suitable aerial and satellite images from various
sources to apply feature extraction methods and comparison between results. Some of
criteria are applied to select a case study such as :diversity of features such as
(buildings, trees, roads, green area), number of features, spatial resolution, contrasting
colors, simplicity and complexity of image.
Three case studies are used to extract building from aerial and satellite images:
- Study area (1) : The study area (1) is an agricultural area in middle area of Gaza
Strip is clipped from aerial photographs (2007) with (0.5 x 0.5 m) spatial
resolution and its coordinates is (34.290 N , 31.260 E). This image contains
number of roads, buildings and agricultural pools as shown in Figure 4.1.
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Figure 4.1: Study Area (1)
- Study Area (2) : The study area (2) is a residential area of Gaza City, specifically
in the Islamic University of Gaza campus. This image is from an aerial
photograph (2010) with (0.125 x 0.125 m) spatial resolution and has coordinates
(34.430 N , 31.510 E) as shown in Figure 4.2.
Figure 4.2: Study Area (2)
- Study Area (3): is a residential area in the United States of America with (0.38 x
0.38 m) spatial resolution as shown in Figure 4.3.
54
Figure 4.3: Study Area (3)
4.2.2 Image Pre-Processing
Preprocessing of image data often will include radiometric correction and geometric
correction. The following subsections illustrate all needed steps of automatic feature
extraction based on Study Area (2) sample shown in Figure (4.2).
4.2.2.1 Geometric Correction
To correct the geometric distortions, one should apply two steps, geo-referencing and
resampling using ARCGIS 10.1 or Erdas 2013 as shown in Figure (4.4).
The geographic space of each dataset was referenced according to four known
coordinates corresponding to the minimum x and y values, the minimum x and
maximum y values, the maximum x and minimum y values, and the maximum x and y
values. Georeferencing is the process of assigning geographic information to an
image. Knowing where an image is located in the world allows information about
features contained in that image to be determined. This information includes location,
size and distance.
55
Figure 4.4: Geo-referencing method & toolbar in ARCGIS 10.1
After correcting the coordinate system, the spatial characteristics of pixels may be
changed. So resampling should be applied to obtain a new image more pronounced in
which all pixels are correctly positioned within the terrain coordinate system to more
accurate feature extraction methods.
4.2.2.2 Radiometric Correction
Radiometric correction involves the processing of digital images to improve the fidelity
of the brightness value magnitudes. Any image contains radiometric errors and
inconsistencies will be referred to as "noise" these errors should be corrected before the
post processing enhancement, extraction and analysis of information from the image.(
Stow, 2001)
The sources of radiometric noise and the appropriate types of radiometric corrections,
partially depend on the sensor and mode of imaging used to capture the digital image
data such as aerial photography, optical scanners, sensors and others.
Improvement quality of images which is used in Study Area (2), radiometric noise
reduction, is performed using ERDAS 2013 as shown in Figure 4.5.
56
Figure 4.5: Noise reduction of Study Area (2)
4.2.2.3 Image Enhancement
Figure 4.6 shows each study area and its corresponding histogram. From this histogram,
Image appearance can be enhanced better before extraction process and can also reveal
whether there is much noise in the image, if the ideal histogram is known.
Figure 4.6: Histogram of study areas
57
Feature Extraction is a combined process of segmenting an image into regions of pixels,
computing attributes for each region to create objects, and classifying the objects to
extract it.
4.3 Feature Extraction Methods
Digitizing is a way of conversion of information from analogously produced graphical
maps to machine readable vector or raster formats. Many methods are used for the
vectorizing process and feature extraction. Manual and automated methods are adopted
in this study to extract features from imagery (Stanley, 2003). Figure 4.7 shows the
methods and programs which are used in this study.
Figure 4.7: Feature extraction methods and programs
4.3.1 Manual Digitizing
Almost all programs of GIS can digitize images using editor toolbar with available
drawing tools (line , polygon , point). The first step in digitizing a map requires
creating feature class into personal geodatabse and adjust all configurations of feature
class such as coordinate system, xy tolerance and add fields database using Arc- catalog
program as shown in Figure 4.8.
Manual Automated
Feature Extraction
Methods
Pixel-Based Object-Based
Barista 2.3.1 Erdas Objective 2013
ENVI 5.0
ArcGIS 10.1
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Figure 4.8: Create feature class in personal geodatabase using Arc-catalge
To start digitizing, the user should use Arc-map window and add feature class with
active editor toolbar as shown in Figure 4.9, then zoom to specific areas on screen and
trace points, lines, or polygons on the map. Because the maps are already in the correct
geographic coordinate system anything digitized on top of the map will also be in the
correct coordinate system.
Figure 4.9: Editor toolbar in Arc-map
Manual digitizing depends on the human visual, focus and ability of the operator to
tracking and digitizing edges of the feature on the suitable location of pixel to get more
accurate. So, the manual feature extraction consume a lot of time as shown in section
59
4.4. Figure 4.10 shows the steps of the manual tracking and digitizing edges of the
building on the map and Figure 4.11 shows manual feature extraction from study areas.
Figure 4.10: Manual tracking and digitizing edges of the buildings
Figure 4.11: Manual feature extraction of study areas
Before
Before
Before
After
After
After
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There are some advantages and disadvantages of manual feature extraction:
Advantages:
- Can be performed on inexpensive equipment.
- Doesn’t need high quality maps.
- Requires little training.
Disadvantages:
- Tedious.
- Time consuming.
4.3.2 Automatic Digitizing
Two methods are used to extract feature from images; one of them is traditional
classification methods, are all pixel-based, do not utilize the spatial and context
information of an object and its surroundings, which has potential to further enhance
digital image classification. The second one is Object based feature extraction which is
a new method that is widely used recently.
4.3.2.1 Object-Based Feature Extraction
Commercial programs are introduced with new tools and developed new algorithms to
extract feature from images such as (ERDAS Imagine 2013, ENVI 5.0, Feature analyst
5.2, Feature extraction 11, FETEX 2.0). The processing that applied to the case study
image using two programs (ENVI 5.0 and ERDAS imagine objective 2013).
A. ENVI 5.0
ENVI® (the Environment for Visualizing Images) is a revolutionary image processing
system. From its inception, ENVI was designed to address the numerous, specific needs
of those who regularly use satellite and aircraft remote sensing data.
ENVI feature extraction consists a combined process of segmenting an image into
regions of pixels, then computing attributes for each region to create objects. The
workflow consists of two primary steps as shown in Figure 4.12: find objects and
extract features. The find objects task is divided into four steps: segment, merge, refine,
and compute attributes. When you complete this task, you will perform the extract
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features task, which consists of supervised or rule-based classification and exporting
classification results to shapefiles and/ or raster images.
Figure 4.12: Feature extraction workflow of ENVI 5.0
In ENVI 5.0, open map and run rule based feature extraction toolbox as shown in Figure
4.13 to start change setting of processing as follow :
- Image Segmentation
Image segmentation is the primary technique that is used to convert a scene or image
into multiple objects. Applying the object-based paradigm to image analysis refers to
analyzing the image in object space rather than in pixel space, and objects can be used
as the primitives for image classification rather than pixels, so image segmentation is
the process of partition an image into segments by grouping neighboring pixels with
similar feature values (brightness, texture, color, etc.)
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Image segmentation can be performed automatically by employing an edge-based
segmentation algorithm that is very fast, familiar end user and only requires one input
parameter (scale level). Adjust the scale level as necessary, values range from 0.0 (finest
segmentation) to 100 (coarsest segmentation; all pixels are assigned to one segment).
Figure 4.13: Object based feature extraction toolbox
Figure 4.14 shows detect boundary of (Tayba building in Islamic University of Gaza)
using edge algorithm at different levels of segmentation.
Figure 4.14: Image segmentation result at different levels
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- Merging Segments
Some of features on the image are larger, textured areas such as trees and building.
Merging Segments used to aggregate small segments within these areas where over-
segmentation may be have a problem. Scale level for merging is a useful option for
improving the delineation of roads, buildings and farms boundaries as it is clearly
shown in Figure 4.15.
Figure 4.15: Merging segments result at different levels
To get the best results, we must try to change scale level in segment setting algorithm
and merge setting algorithm at the same time as shown in Figure 4.16.
Figure 4.16: Optimal segmentation level 82 and merge level 95
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- Supervised Classification
The classification procedure starts with an image segmentation based on the single
intensity band. After segmentation and merge segments, a supervised classification is
performed, that using samples for the different classes (buildings, roads, and farms).
The classifier used is a K nearest neighborhood classifier that defines set of classes
which can be separated automatically. This method is generally more robust than a
traditional nearest-neighbor classifier, since the K nearest distances are used as a
majority vote to determine which class the target belongs to. The K Nearest Neighbor
method is much less sensitive to outliers, noise in the dataset and generally produces a
more accurate classification result compared with traditional nearest-neighbor methods.
Figure 4.17 shows the building class that performed in object base feature extraction
and shape file exported as vector .
Figure 4.17: Classfication method and building extraction
By using the K Nearest Neighbor classification method, an unambiguous classification
result can generally be achieved quite quickly, resulting in improved processing
efficiency, especially in a large projects where manual editing tends to seriously reduce
productivity, and a more sophisticated end product.
K nearest neighbor classification method Shape file exported
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B. ERDAS IMAGINE OJECTIVE 2013
ERDAS IMAGINE, the world’s leading geospatial data authoring system, incorporates
geospatial image processing and analysis, remote sensing, GIS capabilities into a
powerful and convenient package.
IMAGINE Objective in ERDAS 2013 version and versions released later is one of the
software solutions to object-oriented classification and feature extraction available in
the market, which includes an innovative set of tools, enabling geospatial data layers to
be created and maintained using remotely sensed imagery. Combining inferential
learning with expert knowledge in a true object-oriented feature extraction environment,
IMAGINE Objective emulates human visual processing. IMAGINE Objective also
encapsulates vector processing operators to produce GIS-ready data with minimal post
processing.
Pixels are the primitive informational elements in raster imagery and the starting point
of classifications and extraction process. Classifying pixels depends on training pixels
which are identified by the user with training polygons in the imagery. During the
training phase pixels that are representative of the feature of interest may be submitted
to compute Pixel Cue Metrics to train the Pixel Classifier. The overall process flow of
extraction features using imagine objective is shown in Figure 4.18.
Figure 4.18: Erdas objective process work flow diagram
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The basic structure of a feature model showing the linear manner in which the data is
analyzed. Operators are designed as plugins so that can be more easily added as
required for specific feature extraction scenarios, so after start imagine objective should
be add and change the parameter to get the best results. The steps of feature extraction
as follow:
- Add new variable button then select and add raster.
- All configuration and parameter of detect objects and extract features add from
left tree view menu as shown in Figure 4.19.
Figure 4.19: Configurtions tree view menu of imagine objective
- Raster pixel processor (RPP)
Among the functions available in the software to perform classification are; Normalized
Difference Vegetation Index (NDVI), Single Feature Probability (SFP), shadow and
texture. In this study, SFP and shadow function was used to extract the buildings from
images. The definition of training areas for individual buildings as well as for
background pixels is of central importance to the outcome.
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Training areas had been chosen carefully not include any background pixel. During the
training phase, pixels that are representative of the individual buildings, were submitted
to compute pixel cue metrics to train the pixel classifier. Pick several rooftops with
varying shades to get samples representing the range of colors among the building
rooftops as shown in Figure 4.20, then check automatically extract background pixels so
that the SFP classifier will automatically attempt to extract background samples from
the outside of your training samples.
Figure 4.20: Training areas of imagine objective
- Raster object creators (ROC)
In this step, the function ‘segmentation’ was applied which performed Segment an
image into geometric primitives. While it's not necessarily derived from the Pixel
Probability Layer, the raster object segments will have the zonal mean pixel
probabilities as attributes as shown in Figure 22b below. Apply edge Detection should
be checked on to computing the optimal thresholding level as shown in Figure 4.21.
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Figure 4.21: Comparison between edge detection at different thresholding levels
-Raster object operators (ROO)
Probability filter and size filter allow to keep pixel objects with high probability and a
certain amount of pixels only. Size filter was filtered out raster objects that are too small
or too large thus allowing one to restrict the set of raster objects to those of an
appropriate size of individual buildings. Filtering out objects improved efficiency of the
model, since fewer objects are processed in later stages of the model. The output was a
new raster object layer as shown in Figure 4.22c below.
-Raster to vector conversion (RVC)
Output from the step (ROC) to step (ROO) contained pixels that were grouped as raster
objects which had associated probability metrics. With polygon trace raster objects were
automatically vectorized converting objects from the raster domain to the vector domain
as shown in Figure 4.22d below. It takes as input the ROO and converts each raster
object into a vector object as polygon then produces a vector object layer. The following
steps were applied on vectorized objects.
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-Vector object operators (VOO)
This processed the geometric features of the vector objects and stored the probability
value for each feature of each object in the attribute table. In this step the vector objects
were smoothened in shape which accelerates later processing as shown in Figure 4.22e
below.
-Vector object processor (VOP)
This performed classification on the tree vector objects from VOO above which
involved specifying shape and area cues. This was used by object classifier to measure
shape and size property of the building objects and uses the cues to assign a probability
to each object in the group of building vector objects.
-Vector cleanup operator (VCO)
These classes of operators are performed on the vector object layer output from the
object classifier query and are intended for probability thresholding and for cleanup of
the object polygon or polyline footprint. The finish step is Orthogonality to adjust an
object’s polygon to be comprised of all straight lines and right angles (used for
buildings) as shown in Figure 4.22f.
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Figure 4.22: Steps of feature extarction using Erdas ojective 2013
Figure 4.22 shows the pervious steps of building extraction from image. To get the best
results, we build many of scenarios and applied more than 30 trials of change
parameters and configurations.
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4.3.2.2 Pixel-Based Feature Extraction
Many programs are still pixel based to extract feature from images, although they are
old techniques. These programs depend on many of edge detection algorithms such as
(Canny, Sobel, Prewitt, Roberts and others techniques).
This section uses Barista 2.3.1 program to detect and extract features from images,
Barista software was developed for the processing of high-resolution satellite imagery
for the purpose of 3D geopositioning, 3D information extraction, orthoimage generation
and automated edge extraction. This software was developed in Geomatics Department
at University of Melbourne in Australia.
The feature extraction dialog provides a user interface for selecting methods for image
feature extraction and the parameters for these feature extraction methods. Extraction
features process as follows:
1- Import images which should be in (tiff, jpg ,ecw, jpeg, tif ) format.
2- Select region to extract feature from it ,then right click to select extract features
as shown in Figure 4.23.
Figure 4.23: Extract features using Barista program
3- Feature extraction dialog provides a user interface for selecting methods for
image feature extraction and the parameters for these feature extraction methods.
In the group feature extraction mode, two methods for feature extraction can be
selected as shown in Figure 4.24.
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Canny: select this option if you want to extract image edges using the Canny
operator.
Foerstner Operator: Select this option if you want to extract image points
and/or edges using the framework for polymorphic feature extraction that is
often referred to as the Foerstner operator. Different parameters will be
accessible, depending any methods selected.
Figure 4.24: Feature extraction dialog of Barista program
Pixel-based feature extraction method fails in differentiating between road and
buildings. The boundaries between frames and trees are not very clear as shown in
Figure 4.25. Canny feature extraction mode is the best pixel based extraction method
and its results is excellent.
(a) Canny operator (b) Foerstner operator
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Figure 4.25: Feature Extraction using Canny Operator
4.4 Results and Discussion
After applying and testing some of available feature extraction techniques, results of
each method is shown in Table 4.1. Six criteria are used to evaluate and compare
between results of feature extracted from images such as (assessment accuracy, time
spent, cost, end user friendly, image resolution, hardware requirements).
In order to evaluate and compare the accuracy of the feature extraction results created
by the two approaches, pixel-based and object-based, some of buildings has been
surveyed in study area (2) such as Tayba and Al cafateria buildings in the Islamic
University of Gaza campus as shown in Figure 4.26, and some of buildings in study
area (1) to compare area and final shape building extracted.
Figure 4.26: Surveying layout of Tayba building
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Table 4.1: Results of feature extraction methods
Manual Method Automated Method
Object-Based Pixel-Based
Programs ArcGIS 10.1 Erdas Objective 2013 ENVI 5.0 Barista 2.3.1
Study Area (1)
Study Area (2)
Study Area (3)
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The comparison results between area of extracted sample of buildings are shown in
Table 4.2. Note that the pixel-based feature extraction method fails to differentiate
between roads and buildings. The boundaries between frames and trees are not very
clear, also areas of buildings are inaccurate. On the other hand, it shows buildings, trees
and shadow classes very well.
While results of object based feature extraction is very close to the accurate and shows
more clear boundaries between objects. Buildings are clearly selected as objects in the
image. All the features are illustrated with almost exact shape as it is in the ground.
Farms, trees, roads and shadows are all can be clearly seen that no more mix classified.
The margin of error and the difference in the areas either increase or decrease a few, this
indicates that programs depend on object based technology are used new powerful
classification and object detection algorithms. IMAGINE Objective 2013 includes an
innovative set of tools for feature extraction, update and change detection, enabling
geospatial data layers to be created and maintained through the use of remotely sensed
imagery, but dealing with Erdas imagine objective needs more experience.
Envi 5.0 program is very easy to use for extraction of features from images by easy
steps and end user friendly wizard with more accurate results.
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Table 4.2: Comparison between area of extracted sample of buildings
Methods
Surveying
layout Manual
Automated
Object-Based Pixel-Based
Programs ArcGIS 10.1 Erdas 2013 ENVI 5.0 Barista 2.3
Study Area
(1)
Building ID (5) /Area (m2)
310 306 307 321 252
Building ID (2) /Area (m2)
320 321 339 322 273
Study Area
(2)
Tayba building / Area (m2)
1500 1545 1430 1596 1312
Cafateria building / Area (m2)
750 804 804 806 593
Study Area
(3)
Building ID (21) /Area (m2)
Not available 241.33 246.17 257.4 195
Building ID (11) /Area (m2)
Not available 204.66 235.94 215.13 170
From the characteristics of the two extraction methods, in object oriented image
analysis, object is not a single pixel takes part in the classification. Properly performed
segmentation creates good image objects that facilities the extraction from the image.
Traditional feature extraction methods (manual) process depends heavily on human
labor, which makes GIS database development an expensive and time-consuming
operation when performed manually.
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Table 4.3 shows the manual method needs are more time to digitize all objects in image,
where need to 20 minutes using manual digitizing method if compared with a minute
using automatic feature extraction.
Table 4.3: Time spent to extract features from images by different methods
Methods Manual
Automated
Object-Based Pixel-Based
Programs ArcGIS 10.1 Erdas 2013 ENVI 5.0 Barista 2.3
Time spent (Minutes)
Study Area (1) 7 / 13
buildings
1 1 1
Study Area (2) 21/ 5
buildings
1 1 1
Study Area (3) 20 / 30
buildings
1 1 1
Comparison of the result of the accuracy assessment shows that object oriented image
analysis attain higher overall accuracy (94%) compared with (82%) for Pixel-based
classification approach depending on area and shape criteria as shown in Table 4.3.
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Table 4.4: Summary of overall comparison between feature extraction methods
Methods Manual
Automated
Object-Based Pixel-Based
Programs ArcGIS 10.1 Erdas 2013 ENVI 5.0 Barista 2.3
Assessment
accuracy
97 % 93 % 94 % 82 %
Time
consuming
(Minutes)
24 1 1 1
End user
friendly
Yes Yes Yes Yes
Cost More
specialists
One
specialist
One
specialist
One
specialist
High image
resolution
Necessary Necessary Necessary Necessary
Hardware
specifications
Moderate High High Moderate
Finally, object based feature extraction method is more accurate than of pixel based
feature extraction method, where manual digitizing gives excellent results but it
consume more time and effort.
We use in this research the famous of feature extraction programs, such as Erdas 2013
and Envi 2013, It is founded that Envi program is very easy to use for non-specialist
human and the results is accurate, but process of feature extraction need computers with
high specification.
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5 CHAPTER 5: CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion
- Different techniques for feature extraction are deeply discussed.
- Three case study samples are selected to extract buildings, samples were
selected strictly within the specific criteria and different degrees of difficulty.
- Comprehensive comparison is made between those different techniques either
manual ones or automatic (pixel based and object based).
- The study shows that the object based feature extraction method is more
accurate than of pixel based feature extraction method where manual digitizing
gives excellent results but it consume more time and effort.
5.2 Recommendations
Recommend therefore:
- Developing object-based data models that can be used to identify remote sensed
data for more accurate feature extraction.
- Cooperation with local and global teams in automatic feature extraction
techniques.
- Continue working on the development or improvement of existing algorithms to
enhance the percentage of accuracy and speed of data.
- Urged all Gaza municipalities and GIS users to use automatic feature extraction
from images, because it saves time, money and effort.
- Supporting researches and projects in this field from Palestinian universities.
- Work to provide high image resolution to gives more accurate in automatic
feature extraction techniques.
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