b geocarto international - umsl

This article was downloaded by: [69.154.216.150]On: 18 December 2012, At: 10:25Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Geocarto InternationalPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tgei20

Rule-based classification of high-resolution imagery over urban areas inNew York CitySunil Bhaskaran a , Eric Nez a , Karolyn Jimenez a & Sanjiv K.Bhatia ba Department of Chemistry and Chemical Technology, BronxCommunity College of the City University of New York, Bronx, NY,10453, USAb Department of Mathematics & Computer Science, University ofMissouri – St. Louis, St. Louis, MO, 63121, USAAccepted author version posted online: 10 Sep 2012.Version ofrecord first published: 04 Oct 2012.

To cite this article: Sunil Bhaskaran , Eric Nez , Karolyn Jimenez & Sanjiv K. Bhatia (2012):Rule-based classification of high-resolution imagery over urban areas in New York City, GeocartoInternational, DOI:10.1080/10106049.2012.726278

To link to this article: http://dx.doi.org/10.1080/10106049.2012.726278

PLEASE SCROLL DOWN FOR ARTICLE

Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representationthat the contents will be complete or accurate or up to date. The accuracy of anyinstructions, formulae, and drug doses should be independently verified with primarysources. The publisher shall not be liable for any loss, actions, claims, proceedings,demand, or costs or damages whatsoever or howsoever caused arising directly orindirectly in connection with or arising out of the use of this material.

http://www.tandfonline.com/loi/tgei20

http://dx.doi.org/10.1080/10106049.2012.726278

http://www.tandfonline.com/page/terms-and-conditions

http://www.tandfonline.com/page/terms-and-conditions

Rule-based classification of high-resolution imagery over

urban areas in New York City

Sunil Bhaskarana*, Eric Neza, Karolyn Jimeneza and Sanjiv K. Bhatiab

aDepartment of Chemistry and Chemical Technology, Bronx Community College of the CityUniversity of New York, Bronx, NY 10453, USA; bDepartment of Mathematics & Computer

Science, University of Missouri – St. Louis, St. Louis, MO 63121, USA

(Received 10 July 2012; final version received 29 August 2012)

This paper describes the integration of results from different feature extractionalgorithms using spectral and spatial attributes to detect specific urban features.Methodology includes segmentation of IKONOS data, computing attributes forcreating image objects and classifying the objects with fuzzy logic and rule-basedalgorithms. Previous research reported low class accuracies for two specific classes– dark and grey roofs. A modified per-field approach was employed to extracturban features. New rule-sets were used on image objects having similar or near-similar spectral and spatial characteristics. Different algorithms using spectral andspatial attributes were developed to extract specific urban features from a time-series of Multi-Spectral Scanner (MSS) (4 m 6 4 m) IKONOS data. The modifiedapproach resulted in a remarkable improvement in the accuracy of classes thatregistered low spectral seperability and therefore low accuracy. The spectral andspatial based classification model may be useful in mapping heterogeneous andspectrally similar urban features.

Keywords: feature extraction; urban features; automated algorithms, IKONOSdata; New York-New Jersey.

Introduction

Mapping urban features from very high-resolution (VHR) satellite data is essentialfor planning in urban development, emergency management and in managing andmonitoring the environment (Wright 1996, Clark and Jantz 1999, Sanchez 2004,Bhaskaran et al. 2010). The pace of urban development and consequent sprawldemands an analysis of the current spatial and temporal data that can lead to abetter understanding of infrastructural needs, land use planning, imperviousness andmonitoring of water pollution. The rapid development of new residential,commercial, and industrial areas, reduction in green areas, monitoring of pollutionin urban water bodies, flood zone planning, planning emergency evacuation routes,urban housing development and mapping informal settlements are examples ofdynamic urban problems. Satellite data provide a unique synoptic view of changingurban landscapes that is critical in spatial analysis and modelling. Different types ofsatellite systems and sensors have been investigated, studied and analysed to provide

*Corresponding author. Email: [email protected]

Geocarto International

2012, 1–19, iFirst article

ISSN 1010-6049 print/ISSN 1752-0762 online

� 2012 Taylor & Francis

http://dx.doi.org/10.1080/10106049.2012.726278

http://www.tandfonline.com

Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

geospatial solutions to existing and new urban problems (Carlson 2007, Cadenassoet al. 2007).

The recent availability of VHR imagery from satellite sensors such as IKONOSand QuickBird, as well as from digital aerial platforms, provides new opportunitiesfor detailed urban land cover mapping at very fine scales (Zhou and Troy 2008,Bhaskaran et al. 2010). Several studies have investigated the development anddistribution of buildings and land use in a city which is essential for urbanenvironmental investigation and planning (Zhang 2001) and identification ofinformal settlements (Jain 2007). Other studies have demonstrated the applicationof VHR satellites for providing spatial details ideally suited for urban mapping(Thomas et al. 2003, Nichol and Lee 2005). IKONOS and Quickbird images(4 m 6 4 m to 2.5 m 6 2.5 m pixel size) have also been used for mapping urbanimpervious surfaces, roads and buildings (Sawaya et al. 2003, Peteri et al. 2004).

Different unsupervised and supervised methods of classifying remotely senseddata such as the Iterative Self-Organizing Data Analysis Technique (ISODATA),Maximum Likelihood Classification (MLC) and spectral angle mapper (SAM) havebeen used in mapping urban features. A majority of classifiers for urban mapping arebased on spectral analysis which has reported several problems related to mixedpixels, shadows or low spectral separability between classes (Forster 1985,Bhaskaran and Datt 2000, Blaschke and Strobl 2001, Schiewe et al. 2001, Smith2001, Herold and Scepan 2002, Lee and Lathrop 2005, Zhou 2009). The spatialheterogeneity (variations in shape, size, area and texture) of urban features may alsolead to misclassification of urban pixels. The above issues are also present in theanalysis of VHR data. Research in the past (Roberts et al. 2002, Bhaskaran et al.2010) has demonstrated that the spectral resolution of most VHR data was notadequate to map many urban features. For all the above reasons, mapping urbanfeatures by using traditional per-pixel methods has always been a challenge forseveral years. Therefore, there is a strong case for developing innovative methodsthat go beyond the spectral domain and incorporates shape, size, texture and othernon-spectral elements of imagery (Blaschke and Strobl 2001, Herold and Scepan2002, Zhou 2009).

Feature extraction is a rule-based technique where spectral, spatial and texturalattributes are computed for each region to create objects which are then classifiedusing fuzzy logic (Herold and Scepan 2002, Jin and Paswaters 2007, Bhaskaran et al.2010). It is based on human knowledge and reasoning about specific feature types:e.g. roads may appear elongated in shape, buildings may take a rectangular shape,vegetation may have a high normalized difference vegetation index (NDVI) valueand buildings vice-versa and trees are highly textured compared to grass. Spatial,spectral or texture properties of a vector object can be used to classify the object intoa known feature type (ENVI Zoom Tutorial 2010). Feature extraction uses anobject-based approach to classify imagery. In feature extraction, an object is a regionof interest with spatial, spectral (brightness and colour) and/or texture characteristicsthat defines the region. The benefit of an object-based approach is that the objectscan be depicted with a variety of spatial, spectral and textural attributes (de-Koket al. 1999, Baatz and Schape 1999a,b, 2000, Blaschke et al. 2002, Shackelford andDavis 2003, Herold et al. 2003, Laliberte et al. 2004, Yu et al. 2006, Miliaresis andKokkas 2007, Van Coillie et al. 2007, Zhou and Troy 2008, Bhaskaran et al. 2010).Few studies have attempted feature extraction techniques for urban mapping on timeseries of imagery. Furthermore, to the best of our knowledge, we have not come

2 S. Bhaskaran et al.

Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

across any study where an attempt was made to identify appropriate featureextraction algorithms to map specific urban features.

The objective of the study was to map specific urban features using different rule-sets consisting mainly of spectral and spatial characteristics. The second majorobjective was to develop feature extraction algorithms for automated retrieval ofurban objects from a time-series of VHR satellite data.

Study site and spatial characteristics of urban features

Study site consists of boroughs of Queens and Brooklyn (New York City), Paterson(New Jersey) and some parts of New Jersey. Times series of IKONOS images wereused in this study (Figure 1). On all these images, the land use varies from densebuilt up to low-density built up, recreational sites, open spaces, trees and waterbodies (East River and Hudson River). The majority of the land use in theSeptember 2003 image are industrial buildings (60% of the study site) covering anaverage area of 11,056 km2. Of these buildings, approximately 80% are grey roofs,15% dark roofs and 5% white roofs. Grey roofs and white roofs were characterizedby two distinct shapes—rectangular and circular (white roofs only), whereas theirsizes varied across the image. Dark roofs were limited to square and rectangularshapes while their area ranged from 5800 m2 to 1700 m2. The second major land usecategory is commercial (99% grey roofs) which covers approximately 20% of thestudy site. The commercial grey roofs were more or less uniform in shape(rectangular) and area (3500 m2).

Similar spatial patterns were also observed on the September 2001 image withthe exception of industrial roofs, which were much larger in size (grey roofs, darkroofs and white roofs ranging from 22,000 m2 to 5000 m2). In addition, circularwhite roofs that were observed on the September 2003 image were not found inthis region. In the August 2004 image, the general land use consist of residential(50%), recreational (20%) and commercial and industrial (30%). The residentialbuildings (small grey roofs) have uniform shape, pattern and size. Most of theresidential buildings were found in close proximity to independent trees.Recreational areas consist of clusters of trees with uniform pattern, texture andshape. Majority of the roofs consisted of large, medium and small grey roofs withvarying roof shapes. Uniform spatial properties were also observed in the January2008 image with the exception that most of the land use consisted of industrial andcommercial category. The majority of the land in the May 2008 image is coveredby residential buildings (60%), Industrial (20%) and recreational (15%). Theresidential buildings consist of high-rise buildings that are found in clusters. Thereare shadows cast by these buildings which are oriented to the west of thesebuildings. In general, there has been a slight reduction in recreational andindustrial land uses over a period of time. The above spatial characteristics of anurban area are important to record prior to their classification by an object-basedapproach.

Imagery acquisition

Cloud-free and orthorectified IKONOS satellite images were used in the analysis.Time-series of images (September 2001, September 2003, August 2004, January 2008and May 2008) were acquired in geotiff formats. The IKONOS imagery consists of a

Geocarto International 3

Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

single (1 m 6 1 m) panchromatic and four multispectral bands (4 m 6 4 m). Theimages have a radiometric resolution of 11 bits per pixel and were projected to theUniverse Transverse Mercator (UTM), zone 18 and WGS84 datum. Since the imageswere cloud free and covered a small relatively flat area, we did not apply anyatmospheric corrections to the images. These images were provided by a competitivegrant from the GeoEye foundation. The main criteria for selecting the image datesfor the study was based on a broad understanding of the frequency at which landcover changes were recorded in the study area. The data specifications and resolutioncharacteristics of IKONOS are shown in Table 1.

Figure 1. Study site of New York and New Jersey states shown by five IKONOS images.


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

Methodology

Figure 2 shows the methodology used in this study. Our approach was to analyse oneimage acquired in September 2003 over Queens and Brooklyn boroughs of NewYork City and classify urban features. After a detailed visual examination of thestudy area, a seven-class classification format was designed based on the criteria aslaid out by Anderson (1971). The classification scheme was slightly modified to suitthe study, i.e. extraction of specific urban classes. Our steps included segmentation ofthe image by using different scale-level parameters and weights for each class. Imagesegmentation was followed by merging which is an optional step used foraggregating small segments within larger, textured areas such as trees, clouds orfields, where over-segmentation may be a problem. For example, if an image showsthick vegetation, increasing the Scale Level (in the previous Segment step) may notsufficiently delineate tree-covered areas. The results may be over-segmented if you seta small Scale Level value or under-segmented if you set a large Scale Level value(ENVI ZOOM user manual). In this study, we used the merge regularly, e.g. we usedthe merge scale step to delineate highly textured surfaces such as open paved areaswhich were picked up by classification of roofs. After merging, we computed anddetermined appropriate rule-sets for feature extraction. Each set of rules wereassigned appropriate spectral and spatial attributes based on their specific bandreflectivity, absorption features, shape, size and other important non-spectral photo-elements of interpretation. Spectral and spatial attributes such as the average bandratio, minimum maximum band ratios, shape, size, roundness, elongation,rectangular-fit/RECT-FIT and area features were employed in designing the rules.The final set of rules and attributes were developed by using a combination of bothspectral and spatial attributes. The accuracy of the classification result wascalculated by using both primary ground truth data and secondary data that wereacquired either from other merged image data or from existing databases such as theOpen Accessible Space Information System Cooperative (OASIS) that is created andhosted by the Center for Urban research, City University of New York (CUNY).The OASIS website accesses GIS maps and planning data sets from differentagencies (Arnn 1999). We used both raster and vector data (aerial photo images,street and road networks and buildings) from the OASIS website to visually comparethe results from rule-based classification. The accuracy assessment revealed that two

Table 1. Spectral and spatial resolution characteristics of IKONOS satellite data.

Spatial resolution 0.82 m 6 0.82 mSpectral range Pan band: 526–929 nm

Blue band: 445–516 nmGreen band: 506–595 nmRed band: 632–698 nmNear IR: 757–853 nm

Swath width 11.3 kmOff-Nadir imaging Up to 60 degreesDynamic range 11 bits per pixelMission life expected 48.3 yearsRevisit time Approximately 3 daysOrbital altitude 681 kmNodal crossing 10:30 a.m.


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

classes (dark and grey roofs) were lower than the accepted standards and affectingthe overall accuracy of the classification.

In the second part of the methodology, we attempted a modified per-fieldapproach to map the urban features. New set of segmentations and rules weredesigned and four images were classified using an object-based approach. Accuracyassessment was once again calculated for the classifications. The section belowdetails the modified per-field approach.

Figure 2. Study methodology.


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

Modified per-field approach

We used a modified per-field approach (Wu et al. 2008), in which we divided the studyarea into two segments (Image A and Image B). Each segment was characterized byranges of shapes, area and sizes which were included in the thresholds of the spatialand spectral rules. New rules were created and spatial and spectral attributes werecomputed. We segmented the image based on the characteristics of the spatialconfiguration of the urban features. We employed an edge-based segmentationalgorithm which requires only one input parameter (scale level). This algorithmcompared to region-growing algorithm can generate multi-scale segmentation resultsfrom finer to coarser segmentation by suppressing weak edges to different levels(ENVI ZOOM Tutorial). The scale level was used to accurately delineate most of theboundaries for all feature classes. Pixels of small segments were merged with largerones from the same feature class using different merging scale thresholds. Each ofthese pixels was similar with respect to their characteristic property, such as colour,intensity or texture. We created new rule-sets and fuzzy logic for classifying all theremaining four time-series of images. Accuracy assessment of classification on theremaining images revealed higher-class accuracies for the two classes. Detailed rule-sets for mapping specific urban features were created. The segmentation scale,spectral and spatial attributes computed to extract specific urban feature objects frommulti-temporal IKONOS images are summarized in Table 2. The classified featureswere then exported to a vector format for further spatial analysis and modelling. TheGIS layers are being used concurrently in other studies and applications that are beingconducted by the authors. Specific examples of applications/projects include (a) SolarEnergy Potential Mapping in New York, (b) Green Roof Project, (c) Parks Project inNew York and (d) Land use Land cover projects.

The initial and modified per-field approaches are shown in Figure 2. In thefollowing paragraphs, we discuss the specific rule-sets that were used in the retrievalof urban class objects from the time-series of IKONOS Multi-Spectral Scannerimagery.

Development of algorithms by feature extraction method

A rule-based approach employs rule-building that is primarily based on humanknowledge and reasoning about specific feature types. Rule-based classificationemploys ‘fuzzy logic’ which assigns data to a class by a membership function(mathematical model to model underlying spatial data). This method is different totraditional method of classifying an object as fully ‘true’ or ‘false’ (as in binary rules).A mathematical concept for modelling the underlying data distribution to representthe degree than an object belongs to a feature type. Since information extractionfrom remote sensing data is limited by noisy sensor measurements with limitedspectral and spatial resolution, signal degradation from image pre-processing andimprecise transitions between land-use classes. Most remote sensing images containmixed pixels that belong to one or more class. Fuzzy logic helps alleviate thisproblem by simulating uncertainty or partial information that is consistent withhuman reasoning is an important element in rule-based classification. The output ofeach fuzzy rule is a confidence map, where values represent the degree that an objectbelongs to the feature type defined by this rule. In classification, the object is assignedto the feature type that has the maximum confidence value. With rule-basedclassification, the degree of fuzzy logic may be controlled while building rules. In a


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

Table 2. Summary of all spatial and spectral attribute used in six image time series ofIKONOS imagery.

Acquisition date andsegmentation scale Rules sets

Featureextracted

September 200128.8 bandratio [70.1002, 0.4040] AND rect_fit

[0.2124, 0.6995] AND area 4 1401.1438AND avgband_2 [171.6071, 363.0818]

Dark roof

35.4–54.9 minband_3 [60.0000, 198.0000] AND rect_fit[0.2858, 0.9185] AND area [108.6005,132780.1563]

White roof(small andlarge)

33.4 bandratio [70.1170, 70.0391] AND rect_fit[0.4619, 0.8347] AND area [54.1494,10993.1211], elongation 5 2.6873

Gray Grey roof

formfactor [0.2623, 0.7228]19 avgband_NIR [10.0000, 24.7412] Water71.3 avgband_NIR 4 126.0000 AND

maxband_green 5 185.0000Vegetation

25.2 maxband_red [9.0000, 36.0000] ANDminband_NIR 4 15.0000 ANDmax_band_green 5 486.0939 ANDmaxband_green 5 386.0311

Tree

6.2 avgband_green 5 36.0000 ANDintensity 5 0.2579 AND rect_fit 5 0.6727,

Shadow

September 20037.7–15.9 bandratio [70.1482, 0.0212] AND rect_fit

[0.3722, 0.8224] AND area [83.1847,64939.7305] AND avgband_2 [29.7353,240.3088]

Dark roof(large andsmall)

30.4–54.9 minband_3 [60.0000, 198.0000] AND rect_fit[0.2858, 0.9185] AND area [108.6005,132780.1563] AND convexity 5 3.0119

White roof(Large andsmall)

37.5–21.6 bandratio [70.1170, 70.0391] AND rect_fit[0.4619, 0.8347] AND area [54.1494,10993.1211], elongation 5 2.6873

Gray Grey roof(large andsmall)

formfactor [0.2623, 0.7228]25.2 minband_NIR 5 13.0000 Water30.8 avgband_NIR [546.6000, 899.6353] AND

maxband_green 5 852.8510 ANDtx_variance [33.2764, 4231.1709]

Vegetation

3.1 avgband_NIR [426.2471, 634.8588] ANDmaxband_green 5 686.0939 ANDavgband_green[125.5509, 221.1436]roundness [0.1016, .5664]

Tree

16.5 avgband_green 5 134.6919 ANDarea 5 1554.6581

Shadow

rect_fit 4 0.4381 AND elongation 5 7.7944

August 200444.2 bandratio [70.0784, 0.0889] AND rect_fit

[0.2786, 0.8824] AND area [98.1805,11210.2764] AND avgband_2 [281.7678,768.3200]

Dark roof

33–52.9 minband_3 [383.1490, 1588.6666] AND rect_fit[0.3421, 0.8983] AND area [60.2382,7576.2598]

White roof(large andsmall)

(continued)


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

Table 2. (Continued).


Featureextracted

33.4 bandratio [70.1170, 70.0391] AND rect_fit[0.4619, 0.8347] AND area [54.1494,10993.1211], elongation 5 2.6873

Greay roof

formfactor [0.2623, 0.7228]18 minband_NIR 5 11.0000 AND area [100.005,

134201.1543]Water

10.8 avgband_NIR 4 126.0000 ANDmaxband_green 5 185.0000

Vegetation

7.2 avgband_NIR [426.2471, 634.8588] ANDmaxband_green 5 686.0939 ANDavgband_green[125.5509, 221.1436]roundness [0.1016, .5664]

Tree

38 avgband_green 5 134.6919 ANDarea 5 1554.6581

Shadow

rect_fit 4 0.4381 AND elongation 5 7.7944

January 200820.6 bandratio [70.0113, 0.3644] AND rect_fit

[0.4756, 0.9191] AND area [364.6228,150145.1406] AND avgband_2 5 477.7287

Dark roof

33.4–55.9 minband_3 [60.0000, 198.0000] AND rect_fit[0.2858, 0.9185] AND area [108.6005,132780.1563] AND convexity 5 3.0319


43.1 bandratio [70.1170,70.0391] AND rect_fit[0.4619, 0.8347] AND area [54.1494,10993.1211], elongation 5 2.6873

Greay roof

formfactor [0.2623, 0.7228]8.3 avgband_NIR 5 53.0000 AND

avgband_red 5 14.0000Water

32.9 avgband_NIR 4 121.0000 ANDmaxband_green [3.0000, 121.0000] ANDroundness [0.2045, .4356]

Vegetation

25.2 maxband_red [9.0000, 36.0000] ANDminband_NIR 4 15.0000

Tree

16.5 avgband_NIR 5 123.0000 AND avgband_red[1.0000, 14.0000] AND area 5 3367.1592

Shadow

May 200815–28.8 and 44.2 bandratio [70.1040, 0.2725] AND rect_fit

[0.3819, 0.8276] AND area [2959.1409,174942.4219] AND avgband_2 [106.0773,577.3778] AND elongation 5 2.9723 AND;compactness 4 0.1143 AND roundness[0.1016, .5664]

Dark roof(small andlarge)

33–50 minband_3 [40.0000, 148.0000] AND rect_fit[0.4858, 0.9185] AND area [103.6005,132780.1563] AND convexity 5 2.0119


36.5 bandratio [70.1170,70.0391] AND rect_fit[0.4619, 0.8347] AND area [54.1494,10993.1211], elongation 5 2.6873

Greay roof

formfactor [0.2623, 0.7228]50.3 avgband_NIR [15.0000, 28.0000] Water43.1 avgband_NIR 4 126.0000 AND

maxband_green 5 185.0000Vegetation

(continued)


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

typical workflow, rule building begins with one attribute followed by testing itsconfidence in the extraction of the interested feature followed by the use of moreconditions and attributes to filter out all other features from the scene so that you areleft only with your feature of interest (ENVI ZOOM Tutorial).

We developed rules and algorithms for a single IKONOS image that wasacquired on September 2003. Since some classes recorded lower accuracy, weattempted a modified per-field based approach and developed set of revised rules andalgorithms for feature extraction. We tested the robustness of the rule-sets forretrieving specific features by applying them on different sets of IKONOS images(September 2001, August 2004, January 2008 and May 2008). Accuracy assessmentwas once again carried out on the classification results from the modified approach.Expect for shadows, other classes registered improved classification accuracy.

Results – class-wise description of rule-sets and attributes

Grey roof

Grey roofs (area ranging from 800 m2 to 1800 m2) were segmented at a scale of 26.2and smaller segments were merged with larger ones at a merge scale level of 86.2.This successfully delineated all the grey roofs from the image area. Residential greyroofs were delineated using a smaller segmentation scale level of 21.6 for theSeptember 2003 image only. We used spectral attributes of band ratio and spatialattributes of ‘area’, and rectangular-fit to classify grey roofs which has lowreflectance compared to white roofs. Since the grey roof classification also includedsome vegetation, we used the NIR band to isolate vegetation from the classification.We used the same NIR algorithm for extracting vegetation from impervious surfacesfor the September 2001 image but also used the red bands in addition to the NIR.Grey roofs confuse spectrally with other low reflectance objects, e.g. parking lots,play grounds, tennis courts and basketball courts. Therefore, the spectral attributeswere not adequate to delineate grey roofs. Grey roofs also exhibited some genericshape features such as ‘rectangular and square’ (Figure 3) which made a strong casefor using spatial attributes.

We applied spatial attributes of ‘area’, ‘rectangular-fit’, ‘elongation’, ‘compact-ness’ and ‘form factor’ to separate grey roofs from other impervious and irregularlyshaped features such as parking lots and roads. ‘Elongation’ is a shape measure thatgives a measure of the ratio of the major axis of the polygon to the minor axis of thepolygon. ‘Elongation’ attributes were also used to separate grey roofs from roads

Table 2. (Continued).


Featureextracted

7.2 maxband_red [39.0000, 102.0000] ANDminband_NIR 4 73.0000

Tree

39.8 avgband_green 5 23.0000 ANDarea 5 2250355.0000

Shadow cast byvegetation

39.8 minband_red 5 11.0000 ANDavgband_NIR 4 38.0000 ANDmaxband_red 4 194.0000

Shadow cast byman-madestructures


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

and airport runways (e.g. on the January 2008 image). ‘Compactness’ is a shapemeasure that indicates the compactness of the polygon. ‘Compactness’ is calculatedas the ratio of the area of the polygon to the area of the rectangle that contains itcompletely. Form-factor is computed using the following formula (ENVI-Zoom usermanual, 2007). Form factor ¼ 4p (area)/(total perimeter)2. We used both ‘compact-ness’ and ‘form factor’ spatial attributes to isolate grey roofs from other impervioussurfaces such as parking lots specifically for 2001, 2004 and 2008 images.

Dark roof

Dark roofs ranged from large-small-sized dark roofs consisting of an areaapproximately 7094 m2 (areas ranging from 1000 to 5000 m2). Most of the largedark roofs were found to the north-eastern parts of the series of images. However,for the May 2008 image, we used a lower segmentation scale level ranging from 15 to28.8 to delineate smaller roofs while a higher segmentation scale level of 44.2 wasused to delineate the larger dark roofs located to the north eastern parts of theimage. We used spectral attributes of Band Ratio to extract the dark roofs. The Bandratio attribute computes the normalized band ratio between two bands, using thefollowing equation (B27B1)/(B2 þ B1 þ eps) where B1 is the green band, B2 isNIR band and eps is a small number to avoid division by 0 (ENVI ZOOM Tutorial).In addition to the detection of dark roofs, the band ratio attribute also detectsfeature class ‘shadow’ as well as some of the inland water bodies. The additional

Figure 3. Generic shape features of roof.


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

feature classes of shadow and inland water bodies were removed completely by theaverage threshold value of the pixels in the NIR band.

By applying the above attributes, we found that the inland water from East River onthe August 2004 image, the water reservoirs found on the May 2008 image and bridgesand roads (on all images) were also classified as dark roofs due to near-spectralsimilarities to water. Therefore, we employed spatial features ‘area’, ‘rectangular fit/RECT-FIT’, ‘compactness’, ‘roundness’ and ‘elongation’ to separate dark roofs fromroads, bridges and water. ‘Area’ attribute measures the total area of the polygon minusthe area of the holes (values are in map units). The ‘Rectangular fit’ is a shape measurethat indicates how well the shape is described by a rectangle. This attribute compares thearea of the polygon to the area of the oriented bounding box enclosing the polygon.‘Roundness’ is a spatial attribute that compares the area of the polygon to the square ofthe maximum diameter of the polygon. All the images acquired off-nadir exhibitedelongated shadows. To separate shadows from dark roofs, we used ‘elongation’ and‘compactness’ attributes. For the images that were acquired at noon, we used ‘roundness’and ‘rectangular fit’ spatial attributes to extract the dark roofs. Inland water bodies androads that were also extracted as dark roofs were separated using combination of‘elongation’ and ‘area’ spatial attributes. The road networks of the northern parts of theimage were separated from dark roofs using the ‘elongation’ spatial attributes.

White roof

White roofs were scattered across the entire IKONOS images. Most of the white roofsranged from 1400 m2 to 1800 m2 in area and were mostly found in either square orelongated shapes. Apart from white roofs, there were other features also such asaircrafts, tanks and reservoirs (September 2003 and January 2008) images. Asegmentation scale of 33.4–55.9 delineated white roofs. We used the spectral attributeof minband_3 and spatial attributes of rectangular-fit and area to delineate whiteroofs from August 2004, January 2008 and May 2008 images. We also found that the‘Max_band_red’ (maximum value of pixels in red band) ranged from 252 to 1567pixel values was equally effective in extracting white roofs (applied on September 2001and 2003 images). The white roofs were extracted with a few light coloured roofs andto delineate them from the white roofs we used the ‘Avg_band_NIR’ (pixel valuesranging from 146 to 647) (September 2001 and 2003 images).

Spatial attributes such as ‘area’, ‘convexity’ and ‘rectangular fit’ were applied forSeptember 2003, January 2008 and May 2008 images. The spatial attribute ‘convexity’measures the convexity of the image objects (polygons) and they are either convex orconcave. This attribute measures the convexity of the polygon. The convexity value fora convex polygon with no holes is 1.0, while the value for a concave polygon is lessthan 1.0. Convexity ¼ length of convex hull/length (ENVI ZOOM Tutorial).Convexity was applied to delineate white roofs from other open impervious spaces(September 2003, May 2008). ‘Rectangular fit’ was used to prevent round tanks/reservoirs as well as the aircrafts from Teterboro airport, from being misclassified aswhite roofs (January 2008). Figure 4 shows the spectral profiles of three roofs.

Vegetation

The feature class vegetation consisted of trees, grass and recreational areas. Most ofthe vegetation was located in the north-eastern parts of the image (Queens and


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

Brooklyn) and ranged from 140,640 m2 to 863,984 m2 in area. A segmentation scale10.8 to 43.1 delineated most of the vegetation. We used two spectral attributes(‘Avg_band_NIR’ and band ratio) methods for extracting vegetation. The initialapproach was use the ‘Avg_band_NIR’ which classified vegetation but alsoincluded dark and grey roofs. We then used the ‘Max_band_green’ to isolatevegetation from dark and grey roofs. The spectral attribute band ratio is a measureof NDVI formalized using red and NIR bands. We used this spectral attribute toextract vegetation without using any additional spatial attributes (August 2004,May 2008). In some instances, the extraction of vegetation by using this attributewas challenging due to the low separability and spectral confusion particularlybetween vegetation and bare ground (January 2008 image). In these instances, weused spatial attribute of ‘roundness’ to separate the bare ground from vegetation. Inthe September 2003 image, some of the grey roofs were also classified as vegetationdue to the juxtaposition and sometime overlap of grey roofs and vegetation. Toseparate the grey roofs from grass, we applied the ‘texture variance’ attribute, whichis the average variance of pixels comprising the region inside the kernel (ENVIZOOM Tutorial).

Tree

Trees were mostly located in residential neighbourhoods and in parks. Weidentified three broad categories of trees – tree clusters, rows of trees andindependent trees. A segmentation scale level ranging from 3.1 to 25.2 was used todelineate the trees from the images. Separating trees from other vegetation waschallenging since trees are spectrally similar to other types of vegetation. Ourinitial approach was to use the ‘average band NIR’ and ‘max_band_red’ to isolatetrees from grey roofs and other impervious surfaces. We used spectral attributes‘band ratio’, ‘min_band_NIR’ and ‘max_band_green’ to separate grey roofs andother impervious surfaces from trees. The ‘max_band_green’ attribute was onlyused when grey roofs were extracted as trees as was the case in the September2001 and September 2003 images. In another instance, inland water distributaries

Figure 4. Spectral profile of three roofs.


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

of East River, Hudson River and Willow Lake (January 2008) were also classifiedas trees possibly due to the presence of vegetation in the river. Therefore, we usedspectral attribute ‘min_band_NIR’ on the January and May 2008 images forseparating rivers from trees. We used spectral attribute ‘band ratio’ to isolatetrees from dark roofs (August 2004). Although spectral rules extracted mostof the trees, some row and independent trees were not extracted from theimage. We used spatial attributes of ‘roundness’ to extract these tree clusters(September 2003).

Water

Majority of the feature class water consists of lakes, reservoirs, East River, HudsonRiver and Inland distributaries of East and Hudson rivers. A segmentation scalelevel of 8.3–50.3 was sufficient to delineate water. We used spectral attribute‘avg_band_NIR’ to extract the feature class water. In some instances, dark roofswere also extracted as water. On the January 2008 image, we used spectral attributeof ‘avg_band_red’ to separate water from dark roofs. However, on the August 2004image, we used spatial attribute of ‘area’ to delineate dark roofs from water, sinceroofs were smaller in size compared to the river.

Shadow

The segmentation scale level used to delineate shadow ranged from 6.8 to 39.8.Spectral attributes computed to delineate shadows varied from one image to theother. Most of the shadows were classified using spectral attribute ‘avg_band_green’(September 2001, September 2003, August 2004 and January 2008). In some cases,‘avg_band_green’ extracted shadows cast by trees. Therefore, we applied the spectralattribute of ‘band ratio’ to eliminate trees from the classification (August 2004).When we applied the ‘max_band_green’ for extracting shadows, it also extracteddark roofs and inland distributaries of water. We applied the ‘avg_band_ red’attribute to delineate water and dark roofs from the shadow (January 2008). Toclassify shadows cast by built objects, we used spectral attribute ‘min_band_ red’,which classified all urban shadows, dark roofs and water. The ‘avg_band_green’ and‘max_band_red’ attributes were used to eliminate all dark roofs and water,respectively, from the classification (May 2008).

Spectral attributes often misclassified dark roofs and inland distributaries ofrivers as shadows. Thus, we employed spatial attribute of ‘intensity’, which is ameasure of the brightness of an object in combination with the ‘rectangular fit’attribute to isolate shadow from water (September 2001). Spatial attributes of‘rectangular fit’, ‘area’ and ‘elongation’ were also used to isolate shadows from theinland distributaries of the East River (September 2003). The ‘area’ attribute is thetotal area of the polygon minus the area of the holes measured in map units.However, these rule-sets were not able to classify about 5% of the East Riverparticularly the inland distributaries of the East River and the shadows of thebridges on the East River. In addition, the ‘area’ attribute was also applied toprevent some dark roofs from being classified as shadows (August 2004, May 2008).Table 2 provides a summary of rules that were developed to extract specific featuresin an urban environment. Figures 5(a) and (b) display examples of two classes (greyand dark roofs) extracted by the feature extraction method.


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

Figure 5. (a) and (b) Feature extraction results (grey and dark roofs).


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

Accuracy assessment

The classification accuracy was measured by using a standard error matrix. Thematrix was compared by using a pair-wise z-score significance test (Congalton 1991,Congalton and Green 1999). We compared the classification results with groundtruth image data to assess the overall accuracy. An error matrix was computed toobtain the user’s and producer’s accuracy. The producer’s accuracy represents themeasure of omission errors that correspond to those pixels belonging to a class ofinterest that the classifier has failed to recognize. The user’s accuracy, on the otherhand, refers to the measure of commission errors that correspond to those pixelsfrom other classes that the classifier has labelled as belonging to the class of interest(Richards & Jia 1999). We have considered a minimum of 458 pixels in evaluatingthe accuracy as recommended by Congalton and Green (1999). An independentvalidation of the extracted polygons was also performed by exporting vector layerson to a VHR (1 m 6 1 m) fused image (Table 3).

Conclusion and discussion

By using a modified per-field and a rule-based approach, we have developed amethodology to extract and map selected urban features from VHR data. Wetested the robustness of the algorithms by on other images with similar resolutioncharacteristics (4 m 6 4 m). By using rule-sets that included a combination ofspectral and spatial attributes, we were able to successfully extract several urbanfeatures from the IKONOS image. These rule-sets were developed for one imageand then tested on time-series of multi-temporal IKONOS datasets. Some of theserule-sets may be used to extract specific urban features from similar resolutionimagery.

VHR satellite data have high spatial but limited spectral resolution. Further-more, within-field variations in images over urban environments may createproblems in the developing consistent rules across any urban area. But by employinga per-field approach, it is possible to achieve a reasonable consistency in developingautomated rule-sets. However, it important to understand the spatial configurationof urban areas before developing spatial, textural, contextual or relational attributes

Table 3. Classification accuracy – pre- and post-modified approach.

ClassProduceraccuracy

Useraccuracy

Produceraccuracypixels

Useraccuracypixels Overall accuracy

Accuracy of classification prior to the modified approachGreay roof 50.18 100.00 3818/7609 3818/3818 ¼ (3818/7609) 50.1774%Dark roof 79.32 100.00 5116/6450 5116/5116 ¼ (5116/6450) 79.3178%Shadow 66.53 100.00 1161/1745 1161/1161 ¼ (1161/1745) 66.5330%Trees 88.74 100.00 1804/2033 1804/1804 ¼ (1804/2033) 88.7359%Water 98.99 100.00 6107/6169 6107/6107 ¼ (6107/6169) 98.9950%White roof 92.43 100.00 2624/2839 2624/2624 ¼ (2624/2839) 92.4269%Vegetation 99.54 100.00 3442/3458 3442/3442 ¼ (3442/3458) 99.5373%

Accuracy of classification (two classes) post-modified per-field approachDark roof 86.71 100.00 2857/3295 2857/2857 ¼ (2857/3295) 86.7071Gray Grey roof 93.88 100.00 5319/5666 5319/5319 ¼ (5319/5666) 93.8758%


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

for feature extraction. This method will work on images where the variations inspatial characteristics are limited.

The choice of appropriate scale of segments is critical in feature extraction sincethe accuracy of classification will be influenced by the scale of segments. The hete-rogeneity of an urban area presents numerous challenges in determining the rightsegmentation scale. A study was conducted by the author to develop a statisticalmodel for determining the optimal scale parameters for segmentation of urbanfeatures (Bhaskaran et al. 2011). Whilst the study revealed useful information onsegmentation scale for urban classification there is clearly a need for more research inthis area.

The feature class ‘Shadow’ was challenging to extract which may be attributed tothe differences in shape and size of the objects that were casting the shadows on thescene. For instance, the shadows cast by a building over a water body are spectrallydifferent from shadows cast over land. Shadows encountered in land can also befrom either built or natural objects. These variations were responsible for lowaccuracy observed in accuracy assessment of the class ‘Shadow’.

Future work

Recently, we have been working on detection of shadows in IKONOS imagery byusing just the spectral components and we have had some success in doing that(Bhaskaran et al. 2012). As the technique gets more developed, we’ll like to integratethat into this work to make shadow detection more robust. We’ll also like toinvestigate the use of a voting algorithm to provide probabilities of different objectsthat should be helpful in identifying the shallow water bodies – currently a limitationin our work. We plan to extend our techniques to detect specific objects inhyperspectral imagery. The use of hyperspectral data will allow us to extract specificobjects with their unique spectral signatures. We further expect that as thehyperspectral sensors become more common, we may be able to extend our work todevelop techniques to identify smaller objects such as land mines and IEDs fromsensors mounted on land-based vehicles.

Acknowledgements

The IKONOS images that were used in this research were provided by the GeoEyeFoundation, Colorado, USA. GeoEye is one of the major geospatial commercial companies inthe US and world. The authors are grateful to Ms. Elizabeth Doerr from the GeoEyeFoundation for patiently working with the authors in making the correct choices of IKONOSimages.

References

Anderson, J.R., 1971. Land use classification schemes used in selected recent geographicapplications of remote sensing. Photogrammetric Engineering, 37 (4), 379–387.

Arnn, M., 1999. Open accessible space information system cooperative [online]. City Universityof New York (CUNY). Available from: http://www.oasisnyc.net/pages/about_OASIS.html [Accessed 19 January 2010).

Baatz, M. and Schape, A., 1999a. GIS-Aktualisierung und Image Fusion basierend auf einemnueun Verfahren zur objektorientierten und multiskalaren Bildanalyse. In: J. Strobl andT. Blashcke, eds. Angewandte Geographische Informationsverarbeitung X1, Beitrage zumAGIT-Symposium, Salzburg. Karlsruhe: Hervert Wichmann Verlag, 12–23.


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

http://www.oasisnyc.net/pages/about_OASIS.html

http://www.oasisnyc.net/pages/about_OASIS.html

Baatz, M. and Schape, A., 1999b. Object-oriented and multi-scale image analysis in semanticnetworks. In: J. Strobl, T. Blaschke, and G. Griesebner, eds. Proceedings of the 2ndInternational symposium on operationalization of remote sensing, 16–20 August. Enschede:ITC, 12–23.

Baatz, M. and Schape, A., 2000. Multiresolution segmentation—an optimization approachesfor high quality multi-scale image segmentation. In: J. Strobl, ed. AngewandteGeographische Informationsverarbeitung XII AGIT Symposium. Salzburg, Germany, 12–23.

Bhaskaran, S. and Datt, B., 2000. Sub-pixel analysis of urban surface materials: a case studyof Perth, W. Australia. IGARSS 2000, Proceedings of the 2000 IEEE InternationalGeoscience and Remote Sensing Symposium, 4, 535–1537.

Bhaskaran, S., et al., 2012. Mapping shadows from very high resolution data using HSV andedge detection techniques (unpublished).

Bhaskaran, S., Paramananda, S., and Ramnarayan, M., 2010. Per-pixel and object-orientedclassification methods for mapping urban features using Ikonos satellite data. AppliedGeography, 30, 650–665.

Bhaskaran, S., Ramnarayan, M., and Paramananda, S., 2011. Determination of optimal scaleparameters for segmentation of urban features from MSS IKONOS imagery. AsianJournal of Geoinformatics, 11 (2).

Blaschke, T. and Strobl, J., 2001. What’s wrong with pixels? Some recent developmentsinterfacing remote sensing and GIS. GIS Zeitschrift fur Geoinformationssysteme, 6, 12–17.

Blaschke, T., et al., 2002. Object-oriented image processing in an integrated GIS/remote sensingenvironment and perspectives for environment applications. In: A. Cremers and K. Greve(Hrsg.). Umweltinfromation fur Planung, Politik und Offentlichkeit/Environmental Informa-tion for planning, policies and the public. Marburg: Metropolis Verlag, vol. 2, 555–570.

Cadenasso, M.L., Pickett, S.T.A., and Schwarz, K., 2007. Spatial heterogeneity in urbanecosystems: reconceptualising land cover and a framework for classification. Frontiers inEcology and the Environment, 5, 80–88.

Carlson, T.N., 2007. An overview of the ‘‘Triangle Method’’ for estimating surfaceevapotranspiration and soil moisture from satellite imagery. Sensors, 7, 1612–1629.

Clark, D. and Jantz, S.C., 1999. Growth management techniques in the city of Carlsbad.Journal of Urban Planning and Development, 121(1), 11–18.

Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely senseddata. Remote Sensing of Environment, 37, 35–46.

Congalton, R.G. and Green, K., 1999. Assessing the accuracy of remotely sensed data:principles and practices. 2nd ed. Boca Raton, FL: CRC Press, 208 pp.

de-Kok, R., et al., 1999. Object based image analysis of high resolution data in the alpineforest area. In: Proceedings of ISPRS workgroups. Joint WSF ISORS WGI/I, I/3 and IV/4:sensors and mapping from space, 27–30 September, Hanover. [CD-ROM].

ENVI-Zoom Tutorial, 2010. ENVI user guide. Colorado, USA: ITT.Forster, B.C., 1985. An examination of some problems and solutions in monitoring urban

areas from satellite platforms. International Journal of Remote Sensing, 6, 139–151.Herold, M., Gardner, M., and Roberts, D., 2003. Spectral resolution requirements for

mapping urban areas. IEEE Transactions on Geoscience and Remote Sensing, 41 (9), 1907–1919.

Herold, M. and Scepan, J., 2002. Object oriented mapping and analysis of urban land use/cover using IKONOS data. In: Proceedings of 22nd EARSEL symposium geoinformationfor European-wide integration, June, Prague.

Jain, S., 2007. Use of IKONOS satellite data to identify informal settlements in Dehradun,India. International Journal of Remote Sensing, 28 (15), 3227–3233.

Jin, X. and Paswaters, S., 2007. A fuzzy rule base system for object-based feature extractionand classification. In: I. Kadar (ed). Signal processing, sensor fusion, and target recognitionXVI. Proceedings of SPIE. vol. 6567, 65671H–665671H

Laliberte, A.S., et al., 2004. Object-oriented image analysis for mapping shrub encroachmentfrom 1937 to 2003 in southern New Mexico. Remote Sensing of Environment, 93 (1–2),198–210.

Lee, S. and Lathrop, R.G., Jr., 2005. Sub-pixel estimation of urban land cover componentswith linear mixture model analysis and Landsat Thematic Mapper imagery. InternationalJournal of Remote Sensing, 2005, 26 (22), 4885–4905.


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

Miliaresis, G. and Kokkas, N., 2007. Segmentation and object-based classification for theextraction of the building class from LIDAR DEMs. Computers and Geosciences, 33 (8),1076–1087.

Nichol, J. and Lee, C.M., 2005. Urban vegetation monitoring in Hong Kong using highresolution multispectral images. International Journal of Remote Sensing, 2005, 26 (5),903–918.

Peteri, R., Couloigner, I., and Ranchin, T., 2004. Quantitatively assessing roads extractedfrom high resolution imagery. Photogrammetric Engineering and Remote Sensing, 2004, 70(12), 1449–1456.

Richards, J.A. and X. Jia., 1999. Remote sensing digital image analysis. 3rd ed. Germany:Springer, 363 pp.

Roberts, G.P., et al., 2002. Fault slip-rate variations during crustal-scale strain localisation,central Italy. Geophysical Research Letters, 29 (8), 1168; doi: 10.1029/2001Gl013529.

Sanchez, T., 2004. Land use and growth impacts from highway capacity increases. Journal ofUrban Planning and Development, 130 (2), 75–82.

Sawaya, K.E., et al., 2003. Extending satellite remote sensing to local scales: Land and waterresource monitoring using high-resolution imagery. Remote Sensing of Environment, 2003,88 (1–2), 144–156.

Schiewe, J., Tufte, L., and Ehlers, M., 2001. Potential and problems of multi-scalesegmentation methods in remote sensing. GeoBIT/GIS, 6, 34–39.

Shackelford, A.K. and Davis, C.H., 2003. A combined fuzzy pixel-based and object-basedapproach for classification of high-resolution multispectral data over urban areas. IEEETransactions on Geoscience and Remote Sensing, 41 (10), 2354–2363.

Smith, R.B., 2001. Introduction to remote sensing of environment (RSE). Lincoln, Nebraska:MicroImages Inc.

Thomas, N., Hendrix, C., and Congalton, R.G., 2003. A comparison of urban mappingmethods using high resolution digital imagery. Photogrammetric Engineering and RemoteSensing, 69 (9), 963–972.

Van Coillie, F.M.B., Verbeke, L.P.C., and De Wulf, R.R., 2007. Feature selection by geneticalgorithms in object-based classification of IKONOS imagery for forest mapping inFlanders, Belgium. Remote Sensing of Environment, 110 (4), 476–487.

Wright, D.W., 1996. Infrastructure planning and sustainable development. Journal of UrbanPlanning and Development, 122 (4), 111–117.

Wu, S., Wang, L., and Silvan-Cardenas, J., 2008. Per-field urban land use classification basedon tax parcel boundaries. International Journal of Remote Sensing, 28 (12), 2777–2800.

Yu, Q., et al., 2006. Object-based detailed vegetation classification with airborne high spatialresolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing, 72,799–811.

Zhang, Y., 2001. Texture integrated classification of urban treed areas in high-resolutioncolour infrared imagery. Photogrammetric Engineering and Remote Sensing, 67 (12), 1359–1365.

Zhou, N.J., 2009. Semantically enhancing geospatial portals. Position paper. In: USGSSpecialist Meeting of Ontology for the National Map, 3–4 February, Washington, DC,USA.

Zhou, W. and Troy, A., 2008. An object-oriented approach for analyzing and characterizingurban landscape at the parcel level. International Journal of Remote Sensing, 29 (11), 3119–3135.


Dow

nloa

ded

by [

69.1

54.2

16.1

50]

at 1

0:25

18

Dec

embe

r 20

12

b geocarto international - umsl

Documents