analysis of land cover classes using unsupervised and
TRANSCRIPT
Analysis of Land Cover Classes Using Unsupervised and Supervised Classification
of Stennis Space Center (SSC) Image
Shobha Sriharan, Virginia State University
Colleague: DeNeice Guest, Lockheed Martin
NASA Faculty Fellowship Program 2004Stennis Space Center
Analysis of Land Cover Classes Using Unsupervised and
Supervised Classification of Stennis Space Center (SSC) Image
Shobha Sriharan, Virginia State University
Colleague: DeNeice Guest, Lockheed Martin
NASA Faculty Fellowship Program 2004Stennis Space Center
Land Cover Mapping and Applications of Remote Sensing
● Understanding of the type and amount of land cover in an area is an important characteristic from the standpoint of understanding of Earth as a system
● Remote sensing has become a powerful tool for land cover identification and classification of various features of the land surface in an image taken from satellite
● Digital processing of remote sensing data has gained momentum in the last decade
● Earth Science Applications (ESA) Division of NASA is promotion the development of remote sensing technology for providing high quality data products to resolve issues in applied sciences
.● The investment in the development of this technology has
contributed to Precision Agriculture which involves land cover characterization and classification
Objective
To distinguish land cover types of a given satellite image of NASA’s Stennis Space Center (SCC) by using unsupervised and supervised classification
Approach(Materials and Methods)
This study was taken to learn the use of ERDAS Imagine software to classify the data in QuickBird-2 Satellite image of Stennis Space Center for identification and classification of eight terrestrial objects. This image was georeferenced with Satellite IKONOS-2 Panchromatic
Classification of Land Cover
This utilizes a multiband data set to determine the effectiveness in improving the classification. The analysis assesses the utility of unsupervised and supervised classification for distinguishing the land cover.
To identify and isolate particular terrestrial features and proceed in a smooth and systematic manner, the data need to be grouped in a suitable framework. Unsupervised and supervised classification procedures were adopted for identifying land features mentioned below:
Water Mixed TreesDeciduous Trees Scrub GrassShadow GrassConiferous Tress Urban
Software used in this study was ERDAS IMAGINE 8.7
Georeferencing the satellite image of SSC
ISODATA Classifier for ISODATA algorithm to perform unsupervised classification of SSC image
Identification of mixed classes by Cluster Busting Method (Mask)
Labeling of Classes and combining or recoding of identified classes
supervised classification to delineate various land cover features
Evaluation of supervised classification to give a better appearance by Supervised Classification using Majority Filter
Presentation of classified image by a Map Composer
Software and Procedures
Using the ERDAS IMAGINE 8.7 software package, a series of operations were performed for classification of land cover features in the Stennis Space Center (SSC) Image.
Among the three images provided of SSC, the satellite image of SSC taken on February 19, 2004 was selected. In this image, the coniferous trees appear dark red due to the presence of chlorophyll. The deciduous trees appear grey and light green due to shedding of leaves. Satellite images taken in summer were not available. The reflectance from trees is different in Summer and Winter due to the leaves.
Image Classification Process
IMAGERY (Georeferencing)
Image taken by QuickBird 2 Satellite on February 10, 2004, Resolution = 3 meters was georeferenced to the Landstat Pan Image
By georeferencing, the map coordinates were assigned to the image
(ERDAS IMAGINE ERDAS IMAGINE provides standard polynomial georeferencing models)
Image georeferenced with Satellite IKONOS-2 Panchromatic, Acquisition Date/Time:2000-06-23 16:22
Datum: WCGS84 June 23, 2000, Resolution = 1 meter, UTM 16N UGS-84
GCP (Ground Control Points) = 10, Degree to Polynomial,Cubic Convolution, Approximately 10 points were assigned
RMS < 1.0
Image Classification Process A subset of the georeferenced image was created using the subset command under the made under the IMAGINE Interpreter and an inquiry box under the utilities pull-down menu in the viewer.
An unsupervised classification was performed on the image to classify the following eight classes:
Water Mixed TreesShadow Srub GrassConiferous Trees GrassDeciduous Tees, Urban
Signature files were built from the raw data set to give the software an idea of the type of pixel value it must try to match to that category or class.
Coniferous Trees in Red (Chlorophyll)
Deciduous Trees
in Green Shade
Grass
Water
Urban/Road
Deciduous Trees
Mixed Trees
Shadow
Classification In ERDAS IMAGINE, there are two types of classification procedures:
● Supervised● Unsupervised
A supervised classification is performed on this data to delineate various land cover features from data sets.
Using this classification process requires most of the work on the front end of the classification.
Unsupervised ClassificationFirst, an unsupervised classification was performed on the SSC Image using the ISODATA clustering method to classify the image into the desired classes (30) in the final output.
A thematic raster layer was generated using the ISODATA algorithm while running ERDAS IMAGINE
Thirty classes were derived in the classification with:
maximum number of iterations set to 12
convergence threshold set to 0.95
The pixels were identified for each of the categories and they were grouped into land cover categories:
water, shadow, coniferous trees,deciduous trees, mixed trees, scrub grass, grass, and urban
.
Cluster Busting1. To Identify the class that is mixed and needs reclassification 2. Select in IMAGINE, Interpreter – Utilities – Mask
3. The input Mask file is the unsupervised classification that was created from the Unsupervised Image
5. A recode is set up to tell IMAGINE what class has to break out. The Recode button is selected to see all the classes plus a new column called “New Value”.
6. A Mask is created to cut only one class from the original image. To do this, all but one class is set to 0. The class that needs to kept is assigned the value 1. This is done by highlighting in yellow the class which has to break out of the image.
6. A right mouse button is clicked over the very left hand column (Value). Then “Inverse Selection” button on right hand column is clicked.
7. This operation highlights every other class. At this stage, the New Value is set to O at the bottom of the dialog box. Then, the button “Change Selected Rows” is clicked to create the Mask.
Water
Evaluate Classification by Majority Filter
After the classification, there is a tendency to get a salt and pepper effect with some of the classes.
Classification smoothing involves the application of a Majority Filter.
This operation makes the image look good and it smoothes out the classes to give a better appearance.
The Majority Filter is conducted from the Image Interpreter icon to select GIS Analysis Neighborhood. This is done by placing Classified Image in the Input file. The output = new file name.
Majority Filter is selected under the function 5 X 5 size and 7 x 7 from Neighborhood Definition. to compare the results. A Zero set in Stats and OK to run the process.
Area of Land Cover (Classes) in Classified Image
Results and DiscussionsDifferences between Unsupervised and Supervised Classifications
Why the differences occurred between the classification?
Unsupervised Classification
Once the classification was complete there were different numbers of classes for the SCC image. The pixels were identified for each of the categories and they were grouped into land cover categories. These included: water, coniferous and deciduous trees, mixed trees, scrub grass, grass, shadow, and urban.
Supervised ClassificationWhile performing the unsupervised classification, there were a few mixed classes with deciduous and coniferous trees. In some areas of the study area (SSC), the shadow of the tress was combined with Mixed Class of Trees (Coniferous and Deciduous) which gave Mixture. These signatures were taken into account and subtracted from the dataset.
Other issues encountered came from the coniferous and deciduous trees which give of the same reflectance as many of the other vegetation features in the landscape.
The data in unsupervised classified SSC image is not clear. It has generalized tones and reflectance values, which makes it difficult to properly classify every pixel within the image. This causes overestimation of one category and maybe some underestimation of other categories in the process.
ConclusionsThe study led to make following conclusions as to the uses and
effectiveness of ERDAS IMAGINE:
ERDAS IMAGINE is a useful software package for acquiring and classifying Satellite images
Classification is a simple and useful tool, especially when done supervised, for displaying the terrestrial objects and measuring the area occupied by the images
Classification can be done in two ways, Unsupervised (Computer Guided) or Supervised (User Guided)
Classification can be extended into agriculture research and is especially useful for management of pest control strategies and precision farming
Future PlansGround Truthing to asses the accuracy of the classification
Continue the use of ERDAS IMAGINE at Virginia State University (VSU) for classification of various crops planted at VSU’s Randolph Farm
Build a team of faculty and students to conduct research
Integrate the classification procedures in teaching of Ecosystems in the course, Environmental Science
Write proposals to funding agencies such as the USDA Capacity Building, NASA and NOAA for obtaining grants
Collaborate with colleagues who participated in 2004 NFFP to develop proposals in areas of mutual interests.
Learn more about SSC’s Earth Sciences Application’s in Agriculture
Strengthen ongoing collaboration with USDA-ARS Remote sensing Laboratory for extending the work conducted at SSC (precision agriculture). The slides which follow will show examples of applications at the USDA-ARS Lab in Weslaco, Texas ( Dr. James Everitt and Dr. Reginald Fletcher)
Application in AgricultureA - color infrared photograph of big lake B - classified image of big lake
C - QuickBird image of big lake D - classified satellite image
Error matrix table for photograph classification of big lake
Table 2. An error matrix generated from the classification data and ground data for the June 20, 2003 color-infrared photograph of Big Lake on the Welder Wildlife Refuge near Sinton, Texas.
Actual Category
Classified Category
Water Bulrush Spiny aster
Lotus Mixed herbs
Soil/ sparsely veg.
Total
User’s Accuracy
Water Bulrush Spiny aster Lotus Mixed herbs Soil/sparsely veg.
14 0 0 0 0 0
0 12 0 1 0 0
0 0 7 0 0 0
2 1 0 37 0 1
0 1 1 0 10 6
0 0 0 0 0 7
16 14 8 38 10 14
87.5% 85.7% 87.5% 97.4%
100.0% 50.0%
Total Producer’s Accuracy
14 100.0%
13 92.3%
7 100.0%
41 90.2%
18 55.6%
7 100.0%
100
Overall accuracy = 87.0%. Overall Kappa = 0.831.
Table 3. An error matrix generated from the classification data and ground data for the June 7, 2003 QuickBird satellite image of Big Lake on the Welder Wildlife Refuge near Sinton, Texas.
Actual Category
Classified Category
Water Bulrush Spiny aster
Lotus Mixed herbs
Soil/ sparsely veg.
Total
User’s Accuracy
Water Bulrush Spiny aster Lotus Mixed herbs Soil/sparsely veg.
14 0 0 0 0 0
0 7 2 3 1 0
0 2 3 0 0 2
3 1 0 36 0 1
0 4 2 0 3 9
0 0 0 0 1 6
17 14 7 39 5 18
82.4% 50.0% 42.9% 92.3% 60.0% 33.3%
Total Producer’s Accuracy
14 100.0
%
13 53.9%
7 42.9%
41 87.8%
18 16.7%
7 85.7%
100
Overall accuracy = 69.0%. Overall Kappa = 0.598.
Error matrix table for satellite image
classification of big lake
Application in AgricultureA - color infrared photograph of big lake B - classified image of big lake
C - QuickBird image of big lake D - classified satellite image
Table 4. An error matrix generated from the classification data and ground data for the June 20, 2003 color-infrared photograph of Small Lake on the Welder Wildlife Refuge near Sinton, Texas.
Actual Category Classified Category Water Bulrush Lotus Mixed herbs
Total
User’s Accuracy
Water Bulrush Lotus Mixed herbs
11 0 1 0
2 11 0 0
1 0 36 1
0 8 0 9
14 19 37 10
78.6% 57.9% 97.3% 90.0%
Total Producer’s Accuracy
12 91.7%
13 84.6%
38 94.7%
17 52.9%
80
Overall accuracy = 83.8%. Overall Kappa = 0.764.
Error matrix table for photograph classification of small lake
Table 5. An error matrix generated from the classification data and ground data for the June 7, 2003 QuickBird satellite image of Small Lake on the Welder Wildlife Refuge near Sinton, Texas.
Actual Category Classified Category Water Bulrush Lotus Mixed herbs
Total
User’s Accuracy
Water Bulrush Lotus Mixed herbs
11 0 1 0
2 9 0 2
3 1
34 0
0 10 0 7
16 20 35 9
68.8% 45.0% 97.1% 77.8%
Total Producer’s Accuracy
12 91.7%
13 69.2%
38 89.5%
17 41.2%
80
Overall accuracy = 76.3%. Overall Kappa = 0.660.
Error matrix table for satellite classification of small lake
AcknowledgementsNASA Faculty Fellowship Program
NASA’s Earth Sciences Applications Division at SSC
Dr. Ramona Travis, University Affairs Officer, SSC
Dr. Eddie Hildreth and Dr. James Miller
Ms. DeNeice C. Guest, Lockheed Martin at SCC for her guidance in conducting this study, particularly Classification by using ERDAS IMAGINE
Ms. Roxzana Moore, Lockheed Martin at SSC for assistance for learning the nuts and bolts of the softwareDr. Richard Swearingen and Dr. Jerry Griffith who offered valuable feedback and assistance in using the software