Ca

AD

a

ARA

KCSBAF

1

ataot

0d

International Journal of Applied Earth Observation and Geoinformation 21 (2013) 397–408

Contents lists available at SciVerse ScienceDirect

International Journal of Applied Earth Observation andGeoinformation

jo u r n al hom epage: www.elsev ier .com/ locate / jag

lassification method, spectral diversity, band combination and accuracyssessment evaluation for urban feature detection

. Erenerepartment of Geomatics Engineering, Faculty of Engineering and Architecture, Selcuk University, Konya, Turkey

r t i c l e i n f o

rticle history:eceived 6 July 2011ccepted 10 December 2011

eywords:lassificationpectral diversityand combinationccuracy assessmentuture detection

a b s t r a c t

Automatic extraction of urban features from high resolution satellite images is one of the main applica-tions in remote sensing. It is useful for wide scale applications, namely: urban planning, urban mapping,disaster management, GIS (geographic information systems) updating, and military target detection. Onecommon approach to detecting urban features from high resolution images is to use automatic classifica-tion methods. This paper has four main objectives with respect to detecting buildings. The first objectiveis to compare the performance of the most notable supervised classification algorithms, including themaximum likelihood classifier (MLC) and the support vector machine (SVM). In this experiment the pri-mary consideration is the impact of kernel configuration on the performance of the SVM. The secondobjective of the study is to explore the suitability of integrating additional bands, namely first principalcomponent (1st PC) and the intensity image, for original data for multi classification approaches. Theperformance evaluation of classification results is done using two different accuracy assessment meth-ods: pixel based and object based approaches, which reflect the third aim of the study. The objective hereis to demonstrate the differences in the evaluation of accuracies of classification methods. Consideringconsistency, the same set of ground truth data which is produced by labeling the building boundaries inthe GIS environment is used for accuracy assessment. Lastly, the fourth aim is to experimentally evaluatevariation in the accuracy of classifiers for six different real situations in order to identify the impact ofspatial and spectral diversity on results. The method is applied to Quickbird images for various urbancomplexity levels, extending from simple to complex urban patterns. The simple surface type includes aregular urban area with low density and systematic buildings with brick rooftops. The complex surfacetype involves almost all kinds of challenges, such as high dense build up areas, regions with bare soil, andsmall and large buildings with different rooftops, such as concrete, brick, and metal.

Using the pixel based accuracy assessment it was shown that the percent building detection (PBD) and

quality percent (QP) of the MLC and SVM depend on the complexity and texture variation of the region.Generally, PBD values range between 70% and 90% for the MLC and SVM, respectively. No substantialimprovements were observed when the SVM and MLC classifications were developed by the addition ofmore variables, instead of the use of only four bands. In the evaluation of object based accuracy assess-ment, it was demonstrated that while MLC and SVM provide higher rates of correct detection, they alsoprovide higher rates of false alarms.

© 2011 Elsevier B.V. All rights reserved.

. Introduction

Urban object detection from remote sensing images has beenn important research topic in recent decades, since it is one ofhe advanced techniques used to collect large scale data without

ny physical or intimate contact with the object. Identificationf buildings, roads, utilities, and recreational areas is impor-ant for urban planning, GIS updating, disaster management, and

E-mail address: ae76@hotmail.com.tr

303-2434/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.jag.2011.12.008

military target detection. With the availability of very high resolu-tion (VHR) satellite imagery, and the advances in digital processingand analysis techniques, detection of small scale manmade struc-tures have become of great interest. There are a number ofchallenges in the detection of urban objects from high resolutionsatellite data. The first difficulty is related to the object properties.Inherently, manmade structures may be composed of detailed andcomplex surface materials, such as concrete, brick, asphalt, metal,

plastic, glass, shingles and soil. Hence, there is a high spatial andspectral diversity of surface materials. Second, the environs of theobject, such as bare soil, cars, shadows and squares may appearindistinguishable from roads and buildings due to similar reflective

3 Obse

coutam2

tiTmslas2oav1eieai

98 A. Erener / International Journal of Applied Earth

haracteristics. Third, the appearance of buildings or roads may beccluded by shadow or trees, which result in incomplete and irreg-lar shapes on the result map (Song et al., 2006). Another reason ishe mixed pixel problem, which is related to image properties, suchs resolution, sensor type, orientation, quality, dynamic range, illu-ination conditions, weather conditions and seasons (Lo and Choi,

004).Due to the complexity and diversity of the problem domain,

here are various algorithms and methods developed for build-ng extraction (i.e. the detection and delineation of buildings).he most commonly used approach for information extraction isultispectral classification methods (Jensen, 1996). Since remote

ensing images consist of rows and columns of pixels, regard-ess of its spatial context, each image pixel is assigned to a classccording to spectral similarity in a conventional pixel-based clas-ification (Jensen, 1986; Gong et al., 1992; Casals-Carrasco et al.,000; Dean and Smith, 2003). Numerous classification method-logies have been applied in order to detect manmade structuresnd/or to describe the geographic distribution of land cover andarious degrees of success have been recorded (De Fries et al.,998; Borak and Strahler, 1999; Guerschman et al., 2003; Chintant al., 2004; Ouattara et al., 2004). Among these, the most popular

s the maximum likelihood classifier (MLC) method for informationxtraction (Mesev, 2001; Huang et al., 2007; Qian et al., 2007; Sarpnd Erener, 2008; Babu, 2009; Erener and Düzgün, 2009). The SVMs another popular method which has gained extensive applications

Fig. 1. Six different test regions, located in th

rvation and Geoinformation 21 (2013) 397–408

in pattern recognition over the last decades (Gualtieri and Cromp,1999; Brown et al., 2000; Huang et al., 2002; Zhu and Blumberg,2002; Melgani and Bruzzone, 2004; Pal and Mather, 2005; Chi et al.,2008). It is a machine learning algorithm which employs optimiza-tion algorithms to locate the optimal boundaries between classes(Huang et al., 2002). There are a number of publications detail-ing the fundamentals of SVM (see, e.g. Vapnik, 1995; Burges, 1998;Foody and Mathur, 2004; Hsu et al., 2003).

In the literature, there are various studies focusing on the com-parison of classifiers, including the maximum likelihood classifier(MLC), neural network classifiers (NNC), decision tree classifiers(DTC), the support vector machine (SVM), and object-based imageclassification (Huang et al., 2002; Foody and Mathur, 2004; Marcalet al., 2005; Boyd et al., 2006; Aubrecht et al., 2008; Blaschkeet al., 2008; Chi et al., 2008; Dixon and Candade, 2008; Grenieret al., 2008; Platt and Rapoza, 2008; Kavzoglu and Colkesen, 2009;Blaschke, 2010; Otukei and Blaschke, 2010; Tiede et al., 2010;Aytekin et al., 2012). Most of these studies in the literature eval-uated the comparison of classification accuracies simply by usinga pixel based error matrix (Huang et al., 2002; Matinfar et al.,2007; Otukei and Blaschke, 2010) or certain object based accu-racy assessment methods (Beauchemin and Thomson, 1997; Aksoy

et al., 2008; Albrecht et al., 2010). This study, therefore, makes animportant contribution to the issue of methodology, since in addi-tion to the pixel-based method, an object-based error measure wasalso used for the comparison of the algorithms.

e western districts of Ankara province.

Obser

oMiactism

t(sPsbas

A. Erener / International Journal of Applied Earth

Most studies experimentally evaluate the accuracy comparisonf classifiers for a single or limited number of cases (Foody andathur, 2004; Ge et al., 2008). As a result of literature surveying, it

s well established that the selection of a suitable classifier as wells appropriate bands (original or derived) is essential for improvedlassification accuracies (Lu and Weng, 2007). Additionally, in ordero obtain an unbiased error matrix values, it is critical to address thessues of selection of suitable sample size, adoption of appropriateampling scheme, and application of appropriate descriptive andultivariate statistics.To this end, four main objectives were outlined: (1) compare

he performance of the MLC and SVM in the detection of buildings,2) explore the suitability of using the ancillary data for multiclas-ification approaches by including first principal component (1stC) and the intensity image, (3) assess the accuracy of the clas-ification methods using two different approaches, namely pixel

ased and object based approaches, and (4) experimentally evalu-te the variation in the accuracy of classifiers for six different realituations.

Fig. 2. The flow chart foll

vation and Geoinformation 21 (2013) 397–408 399

2. Methods

2.1. The study region and data sets

In order to implement the study, six different small test regionswere selected which are located in the western districts of Ankaraprovince, Turkey (Fig. 1). These regions include the Emniyet (1),Ortadogu (2), Erler (3), Yeni Batı (4), Mehmet Akif Ersoy (5) andOrman C iftligi (6) neighborhoods, respectively (see Fig. 1). In orderto evaluate the performance differences of classification outputsspatially, the implementation of the study was not restricted to onlyone region, since each region is formed by different properties ofsurface features. Regions numbered one to six extend from simpleto complex urban patterns. The simple surface type includes a regu-lar urban area with low density and systematic buildings with brickrooftops. The complex surface type involves almost all kinds of chal-

lenges, such as high dense built up areas, regions with bare soil, andsmall and large buildings with different rooftops, such as concrete,brick and metal. A Quickbird image and a corresponding reference

owed in the study.

4 Obse

mtlhpTc

2

maceobui

sisirfcstspSduouttop

GftctfiPmP

mp

mbn(pBidsfw

00 A. Erener / International Journal of Applied Earth

ap were used in this evaluation study. The Quickbird image ofhe study region was acquired in 2002. The data was composed ofow resolution (2.4 m) multi-spectral (R, G, B and NIR) bands andigh resolution (0.6 m) panchromatic band. The reference map wasroduced by manually labeling the building boundaries in the GIS.he features, which were not well identified from the image, werehecked during field work.

.2. Flow chart of the study

This study is based on the detection of buildings by using theultispectral classifiers, including the MLC and SVM classifiers. In

ddition, the incorporation of ancillary data into the multispectrallassification process was tested. The results were experimentallyvaluated in six different regions in order to identify the impactf spatial and spectral diversity on results. In addition to the pixelased accuracy assessment, an object based assessment was alsosed in order to test the performances. The flowchart of the study

s given in Fig. 2.The characteristics of urban objects are formed not only by their

pectra but also through their structure (Zhang, 1999). That is whyt is important, in land mapping or urban applications, for bothpectral and spatial resolution to be high. Therefore, in this study,nitially, medium resolution multi-spectral imagery (MS) and highesolution panchromatic imagery (PAN) of Quickbird data wereused by using the PANSHARP algorithm (Yun, 2002). As a result, aolor image at the resolution of pan was obtained. Intuitively, theelected image fusion algorithm may be thought to have an effecto the quality of posterior analysis, because the complexity of acene increases with the resolution. However, as Wald et al. (1997)reviously discussed, many of the studies, such as Woodcock andtrahler (1987), Welch et al. (1989), Rowe (1992), and Raffy (1993),emonstrate that the quality of the assessment of a parameter is annpredictable function of the resolution. Among the fusion meth-ds, the most frequently used methods, i.e. the IHS and the PCS,sually distort the spectral characteristics of the original multispec-ral images, to different extents (Shettigara, 1992; Zhang, 1999). Inhis study, the PANSHARP algorithm was considered because it isne of the best merging techniques that preserves the statisticalarameters of the original images (Nikolakopoulos, 2004).

Apart from the original Quickbird Pansharpened bands (Red,reen, Blue, and NIR), two more derivative bands were generated

rom the original data in order to evaluate the incorporation intohe multi classification analysis. These included the first principleomponents (PC1) and the intensity image. PCA analysis was usedo compress the information of a number of bands of imagery intoewer numbers of bands. Hence, the transformed data may be morenterpretable than the original data (Jensen, 1996). The analysis ofCs showed that the first PC in all cases accounted for the maxi-um amount of variance in all multispectral data sets. Therefore,

C1 was used as the additional band in the analysis.The intensity image is calculated by the calculation of the arith-

etical mean of the blue (B), green (G) and red (R) bands of thean-sharpened image.

Four sets of test data were generated in a PCI Geomatica environ-ent in order to evaluate the impact of the inclusion of additional

ands in SVM and MLC classification. In the first set, only the origi-al bands, namely the blue, green, red and NIR bands, were presentSet 1 = B1, B2, B3, B4). In the second set, the first principle com-onent (PC1) was added to the original bands (Set 2 = B1, B2, B3,4, BPCA1). In the third data set, the intensity image was included

n the original bands (Set 3 = B1, B2, B3, B4, BI). Lastly, in the fourth

ata set, both PC1 and intensity were included in the original dataet (Set 4 = B1, B2, B3, B4, BPCA1, BI). After preparation of data setsor each case, region of interest (ROI) data was selected graphicallyithin the images. ROIs are portions of images used for extracting

rvation and Geoinformation 21 (2013) 397–408

statistics for classification. Because the impact of testing trainingdata size on different classification algorithms has been investi-gated in many works (e.g. Hixson et al., 1980; Huang et al., 2002;Foody and Mathur, 2004), only the impact of selection of input vari-able was considered in this study. The same datasets were classifiedusing the MLC, SVM and the proposed algorithm, for four differentsets of data and in six different test regions.

2.3. Image classification

Image classification was performed using the MLC and SVM. Inthe following subsections, a brief explanation of the algorithms isprovided.

2.3.1. Maximum likelihood classification (MLC)One of the most powerful classifiers in common use is that of

the maximum likelihood (Huang et al., 2002; Yan et al., 2006; Qianet al., 2007; Dixon and Candade, 2008; Kavzoglu and Colkesen,2009; Otukei and Blaschke, 2010). The maximum likelihood classi-fier calculates a statistical (Bayesian) probability function from theinputs for classes established from training sites. Each pixel is thenassigned to a class to which it most probably belongs. A detaileddescription and formulation for this classifier is given in Myung(2003).

The advantage of the MLC is that it quantitatively evaluates thevariance and covariance of the category spectral response patternswhen classifying an unknown pixel (Lillesand and Kiefer, 2000).The MLC performs for normally distributed data; however, for datawith a non-normal distribution, the results may be unsatisfactory(Erdas, 1999).

2.3.2. Support vector machines (SVMs)SVMs and have been successfully used for data classification in

the remote sensing arena (Huang et al., 2002; Boyd et al., 2006;Dixon and Candade, 2008; Ge et al., 2008; Yao et al., 2008; Foody,2008; Kavzoglu and Colkesen, 2009; Otukei and Blaschke, 2010).A SVM aims to fit an optimal separating hyper plane or set ofhyper planes in a high or infinite dimensional space, to locate theoptimal boundaries between classes. SVMs, developed by Vapnik(1995) and colleagues, are based on structural risk minimization(SRM). The strategy of SRM is to minimize the upper bound onthe error probability of the classifier or to maximize the marginbetween a separating hyperplane and its closest data points (Huanget al., 2002). A complete mathematical formulation of SVM has beenextensively provided in the literature (e.g. Gunn, 1998; Cristianiniand Shawe-Taylor, 2000; Huang et al., 2002; Foody and Mathur,2004).

In short, assume that the training data with k samples is rep-resented by {xi, yi} i = 1,2,. . .,k, where x ∈ Rn is an n-dimensionalspace, and y ∈ {+1, −1} is class label (Osuna and Freud, 1997). Theseclasses are considered linearly separable if a vector w (which deter-mines the direction of the discriminating plane), perpendicular tothe linear hyper-plane, and a scalar b (the bias that determines thedistance of the hyper plane from the origin) can be defined so thatinequalities (Eqs. (1) and (2)) are satisfied:

wxi + b ≥ 1 for all y = +1; i : e : a member of class 1 (1)

wxi + b ≤ 1 for all y = −1; i : e : a member of class 2 (2)

In some cases, the classes might not be linearly separable (Boyd

et al., 2006). Kernel representations offer a solution in locatingcomplex decision boundaries between classes. The SVM classifierprovides four types of kernels: linear, polynomial, radial basis func-tion (RBF), and sigmoid.

Obser

2

upet

aftbwtMc

S

M

P

Q

adedmrt

fmtodt

gi

s

n

n

TD

A. Erener / International Journal of Applied Earth

.4. Accuracy assessment

For the accuracy assessment, pixel-based and object-based eval-ation metrics were applied. Basically, the ground truth, which isroduced by manually labeling the building boundaries in the GISnvironment, was compared with the output image obtained byhe algorithm.

In pixel-based evaluation (Shufelt and Mckeown, 1993), theccuracy assessment involved computation of true positive (TP),alse positive (FP) and false negative (FN) pixel numbers. TP referso the regions detected correctly as buildings. FP refers to a pixeleing incorrectly detected as buildings. FN refers to the pixels thatere not detected as buildings although they exist in the ground

ruth. Based on these components the split factor, SF, missing factorF, percent of building detection PBD, and quality percent QP were

alculated as follows:

F = FPTP + FP

(3)

F = FNTP + FP

(4)

BD = 100 × TPTP + FN

(5)

P = 100 × TPTP + FP + FN

(6)

In the building detection studies, the output maps of thelgorithms were images where the pixels corresponding to eachetected building were labeled with a unique integer value (Aksoyt al., 2008). These outputs can be considered as objects in the imageata. Therefore, object-based evaluation of classification may beore appropriate than pixel-based evaluation techniques. As a

esult, an object-based criterion was used in this study, in additiono the pixel-based evaluation, for the comparison of the algorithms.

Computations of performance measures were more straight-orward by the object-based error measure, the overlapping area

atrix (OAM) (Beauchemin and Thomson, 1997). In this approach,he ith ground truth object is shown as GTi while the jth outputbject is denoted as Oj. The set of objects in the ground truth isenoted as: GTr = {GT0, GT1, . . . , GTNr } and the set of objects inhe output image is denoted as: Oo = {O0, O1, . . . , ONo }.

Here, GT0 is the background in the ground truth, O0 is the back-round in the algorithm output image, Nr is the number of objectsn the GT, and No is the number of objects in the output image.

The sizes of the areas covered by the objects GTi and Oj and theize of the whole image I can be calculated from the OAM as follows:

(GTi) =N0∑

j=0

Cij (7)

(Oj) =Nr∑

i=0

Cij (8)

able 1istribution of classes in ROI for each test regions.

Class/test region ROI %

I (255,159) II (511,520) III (636,5

Building 383 1772 2007

Forest 456 3091 3136

Road 334 1262 2422

BareSoil NULL 2051 8382

Total % 2.9 3.1 4.7

vation and Geoinformation 21 (2013) 397–408 401

n(I) =Nr∑

i=0

n(GTi) =N0∑

j=0

n(Oj) (9)

Here, Cij is the number of pixels in the ith object in the ground truththat overlap with the jth object in the output image.

By using OAM, every pair of ground truth GTi and output Ojobjects was classified as correct detections, over detections, underdetections, missed detections or false alarms (Hoover et al., 1996)according to a given threshold where T = 0.5 is used in this study asfollows:

Correct detection: A pair of objects GTi and Oj was classified ascorrect detection if

• Cij ≥ T × n(Oj) and• Cij ≥ T × n(GTi)

Over detection: An object GTi and a set of objects Oj1 , . . . , Ojk,

2 ≤ k ≤ No, were classified as over detection if

• Cijt ≥ T × n(Ojt ), ∀t ∈ {1, . . . , k} and

• ∑kt=1Cijt ≥ T × n(GTi)

Under detection: A set of objects GTi1 , . . . , GTik, 2 ≤ k ≤ Nr, and an

object Oj were classified as under detection if

• ∑kt=1Cit j ≥ T × n(Oj), and

• Cit j ≥ T × n(GTit ), ∀t ∈ {1, . . . , k}.

Missed detection: A ground truth Object GTi was classified as amissed detection if it was not included in any instance of correctdetection, over detection or under detection.

False alarm: An output object Oj was classified as a false alarmif it was not included in any instance of correct detection, overdetection or under detection.

3. Application

3.1. Region of interest (ROI) analysis

This study attempted to classify mainly the buildings for six dif-ferent test regions. However, different categories of land use/landcover classes, such as bare soil, vegetation, and road were alsopresent for each test region. Therefore, in addition to the build-ing class, region of interests (ROI) were also collated in terms ofdifferent categories of land use/land cover classes present for eachstudy region. The quality of the training process determines the suc-cess of the classification process; therefore, great effort was spentduring the collection of the training samples. For each class, thetraining samples were selected from the relatively homogenous

environment. The selected samples for each image were used forboth classification methods, which is a straightforward process tocompare the performance of each classification method under samethe sample distribution. Table 1 shows the distribution of classes

39) IV (832,704) V (931,575) VI (759,471)

4897 1788 27154985 1400 35006568 2120 2102

10,425 5362 NULL

4.6 2.0 2.3

4 Observation and Geoinformation 21 (2013) 397–408

iob(rp

tcwcrtbttb

3

oota(swsfia(tantwtCgetmtmbTTdtvc1fiw

4

4

�1ci

Fig. 3. The effect of kernel parameters on accuracy for six different data and four

02 A. Erener / International Journal of Applied Earth

n the ROI. The ROI’s were collected in proportion to the abundancef the classes in the study area. ROI’s used in this study rangedetween 1173 and 26,875 samples (pixels), which is about 2–5%Table 1) of the pixels in the entire sub-scene for six different testegions. Table 1 also presents the dimension of each test regions inixels.

Once trained, the MLC and SVM algorithms were used to classifyhe entire subset for six different test regions and for different bandombinations, respectively. The ROI collected for a study regionas used for both SVM and MLC assessment in order to ensure

onsistency in the analysis. Both SVM and MLC classification algo-ithms were performed in an ENVI 4.3 environment. After obtaininghe classification results for six different test regions and differentand combinations, the result maps were post processed in ordero aggregate the land cover classes other than building. As a result,he classification maps contain two different categories, namelyuilding class and non-building class.

.2. Parameter optimization

It is not crucial to optimize the parameters for the MLC. On thether hand, the SVM requires preliminary evaluation in order toptimize the kernel parameters (Dixon and Candade, 2008). Whilehe SVM is a binary classifier in its simplest form, it can function as

multiclass classifier by combining several binary SVM classifierscreating a binary classifier for each possible pair of classes). In thistudy, a pair wise classification strategy for multiclass classificationas used. In the literature, there have been quite a few comparative

tudies for selecting kernel and kernel parameters for SVM classi-cation (Scholkopf et al., 1999; Cherkassky and Ma, 2004; Dixonnd Candade, 2008). It is known that RBF works well in most casesHuang et al., 2002; Boyd et al., 2006; Ge et al., 2008). Based onhese findings, for the current study, only RBF kernels were used,nd in order to observe their effect on accuracy, different RBF Ker-el parameters were used. The parameters that were varied werehe RBF kernel radius (�) and the regularization parameter (C). �as a value greater than 0. This provided the area of influence that

he particular support vector had over the data space (Dixon andandade, 2008). For the RBF kernel, � was set to 1. C was a valuereater than 0. It controlled the trade-off between allowing trainingrrors and forcing rigid margins. Increasing the value of C increasedhe cost of misclassifying points and caused the SVM to create a

ore accurate model that may not generalize well. C was set tohree different values, namely 100, 500 and 1000. Kernel perfor-

ance was measured using the percentage of buildings detectedetween a classification and a reference map-pixel based accuracy.he effect of kernel parameters on accuracy was presented in Fig. 3.he results presented in Fig. 3 show that the percentage of buildingsetected for different sets of data for each test region changed byhe variation of the C value. The most convenient C value that pro-ided the highest percent of building detection for each test regionan be summarized as: 500, 100, 1000, 100, 1000 and 100 for thest, 2nd, 3rd, 4th, 5th and 6th test regions, respectively. The classi-ers that showed the highest accuracies in each set of test regionsere used for comparative purposes.

. Results

.1. Comparison

The comparison of the MLC and SVM using the RBF kernel, where

takes the value 1 and the C value (500, 100, 1000, 100, 1000 and00 for each test regions, respectively) is undertaken in a statisti-ally rigorous way to provide an objective basis for comment andnterpretation. The algorithms’ performances were tested in a real

different data sets.

Observation and Geoinformation 21 (2013) 397–408 403

ssboCuTo

Tmii0rro

cfdspoffif

fvltbtrQvf

taitatfev

ofib

ftcstpOeftmii

Fig. 4. Percent of building detection (PBD) of classifications developed using twoclassifiers. For six different test regions, Y axis is the PBD values obtained for fourdifferent band combinations (Set 1 = B1, B2, B3, B4, Set 2 = B1, B2, B3, B4, BPCA1,

A. Erener / International Journal of Applied Earth

ituation for six different region types (Fig. 1). Additionally, inclu-ion of additional bands in the original data was also evaluated foroth the MLC and SVM. The ground truth data and the output mapf the methods were analyzed in a GIS and Matlab environment.onsidering consistency, the same set of ground truth data wassed to assess the classification results for accuracy assessment.he performances were computed by evaluating pixel-based andbject-based criteria.

The pixel-based performance evaluation results are given inable 2 and Fig. 4. Table 2 presents the calculated split factor andissing factor rates for each band combination of the MLC and SVM

n six different test regions. These results suggest that low false pos-tive values indicate a decrease in the rate of SF. SF ratios lower than.5 values were obtained for all classification results in the six testegions, except the SVM in TR 1 and TR 6. Additionally, higher MFatios were obtained for the MLC and SVM in TR 4 and TR 5 bybtaining higher values of false negatives in the output maps.

The calculated PBD and QP are illustrated in Fig. 4 in order tolearly present the differences between two algorithms for six dif-erent test regions (TR 1, TR 2, TR 3 and TR 6) and for four differentata sets (Set 1, Set 2, Set 3 and Set 4). The overall results in Fig. 4how that the PBD and QP of two algorithms depend on the com-lexity and texture variation of the region. A high value of PBD wasbtained provided that the classification result gave low values ofalse negatives. Although, the PBD was high, the QP of the classi-cation result may have been low, which is due to high values of

alse positives in the classification result.When the test regions were considered, the patterns observed

rom Fig. 4 indicated that the MLC and SVM generally provided PBDalues approximately or higher than 80%, except TR 5, which wasower than 70%. This may be due to the fact that higher number ofrue positive and lower numbers of false negatives were obtainedy the algorithms. The SVM gave significantly better results thanhe MLC in TR 1, TR 2, TR 4 and TR 6. Although the PBD values wereelatively higher than the MLC, the SVM provided lower values ofP for TR 2 and TR 6. While the MLC provided higher values of PBDalues than the SVM for TR-3 and TR 5, it gave lower values of QPor TR 5.

When the data sets were considered, it was evident from Fig. 4hat no substantial improvements were achieved when the SVMnd MLC classifications were developed by adding more variablesnstead of using only four bands. There were minor differences inhe accuracy of PBD values when PCA1 and Intensity bands weredded, respectively, to the classification. However, while the addi-ion of the PCA1 band to the MLC significantly increased the PBDor TR 3, it decreased the PBD for TR 2 and TR 4. Therefore, it wasvident that the usage of algorithms and impact of input variablesary for different patterns.

The output maps which provided the highest PBD accuracybtained from the band combination for the MLC and SVM classi-ers and the proposed algorithm were also compared by the objectased accuracy assessment.

The object-based correct detection required the computation ofalse alarm, missed detection, and over detection and under detec-ion rates, as described in Section 2.4. In order to evaluate andompare the algorithms’ performances, the percent rates of mea-urements were computed for six different test regions. Accordingo the algorithm detailed in Section 2.4, the results of the OAMarameters vary depending on the selected threshold values. TheAM parameters obtained for O.5 threshold value for six differ-nt test regions were mapped and are shown in Figs. 5 and 6or the MLC and SVM algorithms, respectively. These illustra-

ions show the performance differences (in terms of the correct,

issed, and over and under detected buildings) of each algorithmn the spatial domain by overlaying OAM rates for each objectn each test image. In overall results, it can be concluded that

Set 3 = B1, B2, B3, B4, BI, Set 4 = B1, B2, B3, B4, BPCA1, BI) for maximum likely hood(MLC), support vector machine (SVM) classifications.

404 A. Erener / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 397–408

Table 2Pixel-based performance evaluation.

Data set Classification method Bands Number of pixels Ratio

True positive False positive False negative Split factor Missing factor

Test Region 1 (TR 1) MLC+ B1, B2, B3, B4 3791 3803 603 0.5 0.08B1, B2, B3, B4, BPCA1 3754 4169 640 0.52 0.08B1, B2, B3, B4, BI 3754 4201 640 0.53 0.08B1, B2, B3, B4, BPCA1, BI 3568 5624 826 0.61 0.09

SVMC = 500 B1, B2, B3, B4 3834 2459 560 0.39 0.09B1, B2, B3, B4, BPCA1 3863 2948 531 0.43 0.08B1, B2, B3, B4, BI 3865 3210 529 0.45 0.07B1, B2, B3, B4, BPCA1, BI 3866 3126 528 0.45 0.08

Test Region 2 (TR 2) MLC+ B1, B2, B3, B4 12,447 6455 1373 0.34 0.07B1, B2, B3, B4, BPCA1 12,248 6439 1572 0.34 0.08B1, B2, B3, B4, BI 12,444 6443 1376 0.34 0.07B1, B2, B3, B4, BPCA1, BI 12,242 6394 1578 0.34 0.08

SVM 100 B1, B2, B3, B4 12,563 12,345 1257 0.50 0.05B1, B2, B3, B4, BPCA1 12,566 12,649 1254 0.50 0.05B1, B2, B3, B4, BI 12,547 12,357 1273 0.50 0.05B1, B2, B3, B4, BPCA1, BI 12,568 12,825 1252 0.50 0.05

Test Region 3 (TR 3) MLC+ B1, B2, B3, B4 22,508 5487 4841 0.2 0.17B1, B2, B3, B4, BPCA1 23,691 15,131 3658 0.39 0.09B1, B2, B3, B4, BI 22,506 5484 4843 0.2 0.17B1, B2, B3, B4, BPCA1, BI 23,698 15,097 3651 0.39 0.09

SVMC = 1000 B1, B2, B3, B4 22,610 12,400 4739 0.35 0.14B1, B2, B3, B4, BPCA1 22,620 13,230 4729 0.37 0.13B1, B2, B3, B4, BI 22,461 12,966 4888 0.37 0.14B1, B2, B3, B4, BPCA1, BI 22,511 13,811 4838 0.38 0.13

Test Region 4 (TR 4) MLC+ B1, B2, B3, B4 56,821 9600 15,092 0.14 0.23B1, B2, B3, B4, BPCA1 56,623 29,227 15,290 0.34 0.18B1, B2, B3, B4, BI 56,821 9608 15,092 0.14 0.23B1, B2, B3, B4, BPCA1, BI 56,616 29,229 15,294 0.34 0.18

SVM100 B1, B2, B3, B4 56,824 21,558 15,089 0.27 0.19B1, B2, B3, B4, BPCA1 56,784 21,274 15,129 0.27 0.19B1, B2, B3, B4, BI 56,760 21,155 15,153 0.27 0.19B1, B2, B3, B4, BPCA1, BI 56,782 21,741 15,131 0.28 0.19

Test Region 5 (TR 5) MLC+ B1, B2, B3, B4 17,889 9346 8337 0.34 0.31B1, B2, B3, B4, BPCA1 17,882 9498 8344 0.35 0.34B1, B2, B3, B4, BI 17,892 9345 8334 0.34 0.31B1, B2, B3, B4, BPCA1, BI 17,875 9527 8351 0.35 0.31

SVM1000 B1, B2, B3, B4 17,661 6872 8565 0.28 0.35B1, B2, B3, B4, BPCA1 17,641 6814 8585 0.28 0.35B1, B2, B3, B4, BI 17,611 6680 8615 0.27 0.35B1, B2, B3, B4, BPCA1, BI 17,642 6833 8584 0.28 0.35

Test Region 6 (TR 6) MLC+ B1, B2, B3, B4 39,630 21,839 7629 0.36 0.12B1, B2, B3, B4, BPCA1 39,746 21,976 7513 0.36 0.12B1, B2, B3, B4, BI 39,630 21,870 7629 0.36 0.12B1, B2, B3, B4, BPCA1, BI 39,742 21,964 7517 0.36 0.12

SVM100 B1, B2, B3, B4 40,767 41,322 6492 0.50 0.08B1, B2, B3, B4, BPCA1 40,786 42,564 6473 0.51 0.08

40,40,

tafa

ptp0tcadgdtc

B1, B2, B3, B4, BI

B1, B2, B3, B4, BPCA1, BI

he buildings with tiled roofs can be correctly detected by alllgorithms in most of the cases; however, some bare ground sur-aces, park regions or road paths are mostly false detected by thelgorithms.

In order to provide a more rigorous evaluation of algorithms, theercent rates of OAM measurements should also be assessed by ahreshold value in a continuous range. Therefore, in addition to theerformance evaluation in spatial domain for the threshold value.5, the performance was also illustrated by a graphical presenta-ion for the threshold value between 0 and 1. As a result of theseomputations, it was seen that the algorithm results did not provideny over or under detections for any test regions, which might beue to relatively large spacing between the buildings. Therefore, the

raphical presentation of correct detection, false alarm and missedetection rates are displayed in Figs. 7–9. Here, the OAM parame-ers are displayed as percentages in order to provide a reasonableomparison of the algorithms.

792 42,407 6467 0.51 0.08795 43,623 6464 0.52 0.08

The higher values of correct detection in Fig. 7 represent betterperformance (Aksoy et al., 2008) for the algorithms. In Fig. 7, it isclear that the MLC and SVM algorithm results gave higher rates ofcorrect detection, in the range of 80–100%, for all test regions forthreshold values of less than 0.6.

It is stated in Aksoy et al. (2008), that lower values of false alarmand missed detection rates indicate better performance. However,here it should also be clearly identified that because of object-basedanalysis, false alarms could provide higher rates. This is due to thefact that even very small features in the dimensions (e.g. one pixelwide object) were counted as an object for false alarm. For exam-ple, for TR 5, 23 buildings exist in the ground truth data. After theMLC process, 1070 output objects were detected in the output; 22

of these objects were correctly detected buildings. However, therest of the 1049 objects counted as false alarms, regardless of thedimension of each feature. Therefore, higher rates of false alarmsshould not mislead the interpretation of the analysis of results.

A. Erener / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 397–408 405

gions

Woft

Fig. 5. The accuracy assessment of MLC in six different test re

ith respect to these explanations, Fig. 8 presents the percentagef false alarms, and clearly shows that the MLC and SVM providealse alarm rates of higher than 75% for threshold values of lesshan 0.7.

Fig. 6. The accuracy assessment of SVM in six different test regions

by the object based measure OAM for the 0.5 threshold value.

5. Discussion and conclusion

All algorithms have pros and cons and there is no one algo-rithm superior to all others. In order to effectively detect urban

by the object based measure OAM for the 0.5 threshold value.

406 A. Erener / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 397–408

nd tru

faaaooo

Fig. 7. Object-based correct detection rates, in percent to total grou

eatures, one must take into consideration all the likely strengthnd weaknesses, weigh them carefully, and then choose the bestlgorithm for a given task. The choice of a particular classification

lgorithm depends on the nature of input data and the desiredutput. Although, it is very difficult to provide some reflectionsn the selection of the best algorithm, this study presents someutputs that may guide the readers on not only the classification

Fig. 8. Object-based false alarm rates in percent fo

th objects, obtained for MLC and SVM for six different test regions.

method, but also for spectral diversity, band combination and accu-racy assessment for urban feature detection. Depending on theoutput of this study, we can initially say that, the quality of the

training effort required in supervised classification, determines thesuccess of the classification stage. The selection of both representa-tive and complete samples affects the training quality. Additionally,the performances of the classification algorithms were closely

r MLC and SVM for six different test regions.

A. Erener / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 397–408 407

ercen

dtbddba

mftipbrtrtratterrIss

opmcabdf

Fig. 9. Object-based missed detection rates in p

ependent on the surface materials, shape property and the spec-ral reflectance similarity of objects in the test regions. Therefore,y using six different test regions in the study, the nature of inputata was also tested. While pixel based MLC and SVM provide betteretection rates for buildings with brick rooftops and simple shapeduildings, neither are successful in detecting different surface typesnd complex shaped buildings.

By using two different pixel-based and object-based evaluationetrics, the performance of classification results was evaluated

rom two different aspects. In pixel-based evaluation, it was seenhat the PBD and QP of three algorithms depend on the complex-ty and texture variation of the region. Generally, the MLC or SVMrovide higher PBD values, which might be due to the higher num-er of true positives and lower numbers of false negatives. Thisesult was also supported by the object-based accuracy evalua-ion. In object-based assessment, higher rates of correct detectionange were obtained when the evaluation was based on percent ofotal output objects. The MLC and SVM show higher rates of cor-ect detection due to lower rates of missed detection. Here, it islso important to indicate that the classification maps obtained byhe MLC and SVM provide higher numbers of false alarms besideshe correct detection. This is due to of the algorithm consideringven very small sized features as false alarms. This unfavorableesult may be eliminated by the addition of a threshold for the algo-ithm in the consideration of false alarms for output object regions.n this case, the features smaller than the determined thresholdize (e.g. 10 pixels) may not be counted as false alarm in furthertudies.

The SVM gave significantly better results than the MLC in fourut of six test regions (TR 1, TR 2, TR 4 and TR 6) according toixel-based evaluation. However, implementation of the SVM is farore complicated than the MLC because the SVM is sensitive to the

hoice of the mapping kernel. No substantial improvements were

chieved when the SVM and MLC classifications were developedy adding more variables instead of using only four bands for sixifferent test regions. Future study would involve testing the per-ormances of classification methods when different variables are

t for MLC and SVM for six different test regions.

included in classifications other than the selected variables, PCA1and intensity image.

As a result, it can be concluded that it is crucial to conduct var-ious classification algorithms and extensive accuracy assessmentanalysis in order to present a comparative and comprehensive out-put to end users. Accordingly, this type of research and resultantinformation is critical and can be used as a base study for fur-ther studies in the detection of urban features in the utilizationof remotely sensed data.

References

Aksoy, S., Ozdemir, B., Eckert, S., Kayitakire, F., Pesarasi, M., Aytekin, O., Borel, C.C.,Cech, J., Christophe, E., Duzgun, H.S.B., Erener, A., Ertugay, K., Hussain, E., Inglada,J., Lefevre, S., Ok, O., San, D.K., Sara, R., Shan, J., Soman, J., Ulusoy, I., Witz, R., 2008.Performance evaluation of building detection and digital surface model extrac-tion algorithms: outcomes of the PRRS 2008 algorithm performance contest. In:Proc. of IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2008),Tampa, FL, USA December 7, 2008.

Albrecht, F., Lang, S., Hölbling, D., 2010. Spatial Accuracy Assessment of ObjectBoundaries for Object-Based Image Analysis. The International Archives ofthe Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.38–4/C7.

Aubrecht, C., Steinnocher, K., Hollaus, M., Wagner, W., 2008. Integrating earth obser-vation and GIScience for high resolution spatial and functional modeling ofurban land use. Computers, Environment and Urban Systems 33 (1), 15–25.

Aytekin, Ö., Erener, A., Ulusoy, I., Düzgün, H.S.B., 2012. Unsupervised buildingdetection in complex urban environments from multispectral satellite imagery.International Journal of Remote Sensing 33 (7), 2152–2177.

Babu, G.B., 2009. Classification of urban features using airborne hyperspectral data.Dissertation, Indiana State University, December 2009.

Beauchemin, M., Thomson, K.P.B., 1997. The evaluation of segmentation resultsand the overlapping area matrix. International Journal of Remote Sensing 18,3895–3899.

Blaschke, T., Lang, S., Hay, G.J. (Eds.), 2008. Object Based Image Analysis. Springer,Heidelberg/Berlin/New York, p. 817.

Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS Interna-tional Journal of Photogrammetry and Remote Sensing 65 (1), 2–16.

Borak, J.S., Strahler, A.H., 1999. Feature selection and land cover classification of a

MODIS-like data set for a semiarid environment. International Journal of RemoteSensing 20, 919–938.

Boyd, D.S., Sanchez-Hernandez, C., Foody, G.M., 2006. Mapping a specific class forpriority habitats monitoring from satellite sensor data. International Journal ofRemote Sensing 27 (13), 2631–2644.

4 Obse

B

B

C

C

C

C

C

D

D

D

EE

F

F

G

G

G

G

G

G

H

H

H

H

H

J

K

L

L

Zhang, Y., 1999. A new merging method and its spectral and spatial effects. Interna-

08 A. Erener / International Journal of Applied Earth

rown, M., Lewis, H.G., Gunn, S.R., 2000. Linear spectral mixture models and sup-port vector machines for remote sensing. IEEE Transactions on Geoscience andRemote Sensing 38 (September (5)), 2346–2360.

urges, C.J.C., 1998. A tutorial on support vector machines for pattern recognition.Data Mining and Knowledge Discovery 2, 121–167.

asals-Carrasco, P., Kubo, S., Babu Madhavan, B., 2000. Application of spectral mix-ture analysis for terrain evaluation studies. International Journal of RemoteSensing 21 (16), 3039–3055.

herkassky, V., Ma, Y., 2004. Practical selection of SVM parameters and noise esti-mation for SVM regression. Neural Networks 17, 113–126.

hi, M., Feng, R., Bruzzone, L., 2008. Classification of hyperspectral remote-sensingdata with primal SVM for small-sized training dataset problem. Advances inSpace Research 41, 1793–1799.

hintan, A.S., Arora, M.K., Pramod, K.V., 2004. Unsupervised classification of hyper-spectral data: an ICA mixture model based approach. International Journal ofRemote Sensing 25, 481–487.

ristianini, N., Shawe-Taylor, J., 2000. An Introduction to Support Vector Machinesand Other Kernel-Based Learning Methods. Cambridge University Press, Cam-bridge.

e Fries, R.S., Hansen, M., Townshend, J.R.G., Sohlberg, R., 1998. Global land coverclassifications at 8 km spatial resolution: the use of training data derived fromLandsat imagery in decision tree classifiers. International Journal of RemoteSensing 19 (16), 3141–3168.

ean, A.M., Smith, G.M., 2003. An evaluation of perparcel land cover mapping usingmaximum likelihood class probabilities. International Journal of Remote Sensing24 (14), 2905–2920.

ixon, B., Candade, N., 2008. Multispectral landuse classification using neural net-works and support vector machines: one or the other, or both? InternationalJournal of Remote Sensing 29 (4), 1185–1206.

rdas, Inc., 1999. Erdas Field Guide. Erdas, Inc., Atlanta, GA.rener, A., Düzgün, H.S.B., 2009. A methodology for land use change detection of

high resolution pan images based on texture analysis. Italian Journal of RemoteSensing 41 (2), 47–59.

oody, G.M., Mathur, A., 2004. A relative evaluation of multiclass image classifica-tion by support vector machines. IEEE Transactions on Geoscience and RemoteSensing 42, 1335–1343.

oody, G.M., 2008. RVM-based multi-class classification of remotely sensed data.International Journal of Remote Sensing 29, 1817–1823.

e, Q.Z., Ling, Z.C., Qiong, L., Hui, X.X., Zhang, G., 2008. High efficient classificationon remote sensing images based on SVM. In: Proc. of the International Archivesof the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.XXXVII, Part B2, Beijing.

ong, P., Marceau, D., Howarth, P.J., 1992. A comparison of spatial feature extractionalgorithms for land use classification with SPOT HRV data. Remote Sensing ofEnvironment 40, 137–151.

renier, M., Labrecque, S., Benoit, M., Allard, M., 2008. Accuracy assessment methodfor wetland object-based classification. In: Proc. GEOBIA, 2008—Pixels, Objects,Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century, pp.285–289.

ualtieri, J.A., Cromp, R.F., 1999. Support vector machines for hyperspectral remotesensing classification. Proceedings of the SPIE 3584, 221–232.

uerschman, J.P., Paruelo, J.M., Di Bella, C., Giallorenzi, M.C., Pacin, F., 2003. Landcover classification in the Argentine Pampas using multi-temporal Landsat TMdata. International Journal of Remote Sensing 24 (17), 3381–3402.

unn, S., 1998. Support vector machine for classification and regression. TechnicalReport, Image Speech and Intelligent Systems Research Group, University ofSouthampton.

ixson, M., Scholz, D., Fuhs, N., Akiyama, T., 1980. Evaluation of several schemesfor classification of remotely sensed data. Photogrammetric Engineering andRemote Sensing 46, 1547–1553.

oover, A., Jean-Baptiste, G., Jiang, X., Flynn, P.J., Bunke, H., Goldgof, D.B., Bowyer,K., Eggert, D.W., Fitzgibbon, A., Fisher, R.B., 1996. An experimental comparisonof range image segmentation algorithms. IEEE Transactions on Pattern Analysisand Machine Intelligence 18 (7), 673–689.

su, C.W., Chang, C.C., Lin, C.J., 2003. A practical guide to support vector classi-fication. Technical Report, Department of Computer Science and InformationEngineering, National Taiwan University. Available online at: http://www.csie.ntu.edu.tw/∼cjlin/papers/guide/guide.pdf (accessed September 01, 2010).

uang, C., Davis, L.S., Townshed, J.R.G., 2002. An assessment of support vectormachines for land cover classification. International Journal of Remote sensing23, 725–749.

uang, X., Zhang, L., Li, P., 2007. Classification and extraction of spatial features inurban areas using high-resolution multispectral imagery. IEEE Geoscience andRemote Sensing Letters 4 (2), 260–264.

ensen, J.R., 1986. Introductory Digital Image Processing: A Remote Sensing Per-spective. Prentice-Hall, Englewood Cliffs, NJ, ISBN:0-13-205840-5, p. 172,199.

avzoglu, T., Colkesen, I., 2009. A kernel functions analysis for support vectormachines for land cover classification. International Journal of Applied EarthObservation and Geoinformation 11, 352–359.

illesand, T.M., Kiefer, R.W., 2000. Remote Sensing and Image Interpretation. John

Wiley &Sons, Inc, ISBN 978-0-470-05245-7, p. 534.

o, C.P., Choi, J., 2004. A hybrid approach to urban land use/cover mapping usingLandsat 7 Enhanced Thematic Mapper Plus (ETM +) images. International Journalof Remote Sensing 25 (14), 2687–2700.

rvation and Geoinformation 21 (2013) 397–408

Lu, D., Weng, Q., 2007. A survey of image classification methods and techniques forimproving classification performance. International Journal of Remote Sensing26 (5), 823–870.

Marcal, A.R.S., Borges, J.S., Gomes, J.A., Pinto Da Costa, J.F., 2005. Land cover updateby supervised classification of segmented ASTER images. International Journalof Remote Sensing 26 (7), 1347–1362.

Matinfar, H.R., Sarmadian, F., Alavi Panah, S.K., Heck, R.J., 2007. Comparisons ofobject-oriented and pixel-based classification of land use/land cover types basedon Lansadsat7 Etm+ spectral bands (case study: arid region of Iran). American-Eurasian Journal of Agricultural and Environmental Science 2 (4), 448–456.

Melgani, F., Bruzzone, L., 2004. Classification of hyperspectral remote sensing imageswith support vector machines. IEEE Transactions on Geoscience and RemoteSensing 42 (August (8)), 1778–1790.

Mesev, V., 2001. Modified maximum likelihood classifications of urban land use:spatial segmentation of prior probabilities. Geocarto International 16, 39–46.

Myung, I.J., 2003. Tutorial on maximum likelihood estimation. Journal of Mathemat-ical Psychology 47, 90–100.

Nikolakopoulos, K.G., 2004. Pansharp vs. wavelet vs. PCA fusion technique foruse with Landsat ETM panchromatic and multispectral data. Image and Sig-nal Processing for Remote Sensing, Proceedings of the SPIE 5573, 30–40,doi:10.1117/12.565726.

Osuna, E.E., Freud, R., 1997. Support vector machines: training and applications. A.I.Memo No. 1602, C.B.C.L. Paper No. 144. Massachusetts Institute of Technologyand Artificial Intelligence Laboratory, Massachusetts.

Otukei, J.R., Blaschke, T., 2010. Land cover change assessment using decision treessupport vector machines and maximum likelihood classification algorithms.International Journal of Applied Earth Observation and Geoinformation 12,27–31.

Ouattara, T., Gwyn, Q.H.J., Dubois, J.M.M., 2004. Evaluation of the runoff potential inhigh relief semi-arid regions using remote sensing data: application to Bolivia.International Journal of Remote Sensing 25, 423–435.

Pal, M., Mather, P.M., 2005. Support vector machines for classification in remotesensing. International Journal of Remote Sensing 26 (5), 1007–1011.

Platt, R.V., Rapoza, L., 2008. An evaluation of an object-oriented paradigm for landuse/land cover classification. The Professional Geographer 60 (1), 87–100.

Qian, J., Zhou, Q., Hou, Q., 2007. Comparison of pixel-based and object-oriented clas-sification methods for extracting built-up areas in aridzone. In: ISPRS Workshopon Updating Geo-spatial Databases with Imagery & The 5th ISPRS Workshop onDMGISs, Urumchi, Xingjizng, China, August 28–29, 2007, pp. 163–170.

Raffy, M., 1993. Remotely-sensed quantification of covered areas and spatial reso-lution. International Journal of Remote Sensing 14 (1), 135–159.

Rowe, C.M., 1992. Incorporating landscape heterogeneity in land surface albedomodels. Journal of Geophysical Research 98 (3), 5037–5044.

Sarp, G., Erener, A., 2008. Land use detection comparison from satellite images withdifferent classification procedures. In: Proc. of the International Archives of thePhotogrammetry, Remote Sensing and Spatial Information Sciences. Commis-sion WG IV/4, vol. XXXVII, Part B4, Beijing, pp. 555–558.

Scholkopf, B., Burges, C., Smola, A., 1999. Advances in Kernel Methods Support VectorLearning. MIT Press, Cambridge, MA.

Shettigara, V.K., 1992. A generalized component substitution technique for spa-tial enhancement of multispectral images using a higher resolution data set.Photogrammetric Engineering and Remote Sensing 58, 561–567.

Shufelt, J.A., Mckeown, D.M., 1993. Fusion of monocular cues to detect man-madestructures in aerial imagery. CVGIP: Image Understanding 57 (3), 307–330.

Song, Z., Pan, C., Yang, Q., 2006. A Region-Based Approach to Building Detectionin Densely Build-Up High Resolution Satellite Image, 1-4244-0481-9/06/$20.00C2006 IEEE.

Tiede, D., Lang, S., Albrecht, F., Hölbling, D., 2010. Object-based class modeling forcadastre-constrained delineation of geoobjects. Photogrammetric Engineeringand Remote Sensing 76, 193–202.

Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, NewYork.

Wald, L., Ranchin, T., Mangolini, M., 1997. Fusion of satellite images of differentspatial resolutions: assessing the quality of resulting images. PhotogrammetricEngineering and Remote Sensing 63 (6), 691–699.

Welch, R.M., Navar, M.S., Sengupta, S.K., 1989. The effect of spatial resolution uponthe texture-based cloud field classifications. Journal of Geophysical Research 94(14), 767–781.

Woodcock, C.E., Strahler, A.H., 1987. The factor of scale in remote sensing. RemoteSensing of Environment 21, 311–332.

Yan, G., Mas, J.F., Maathuis, B.H.P., Xiangmin, Z., Van Dijk, P.M., 2006. Comparison ofpixel-based and object-oriented image classification approaches—a case studyin a coal fire area, Wuda, Inner Mongolia, China. International Journal of RemoteSensing 27 (18), 4039–4055.

Yao, X., Tham, L., Dai, F.C., 2008. Landslide susceptibility mapping based on supportvector machine: a case study on natural slopes of Hong Kong, China. Geomor-pholog 101, 572–582.

Yun, Z., 2002. A new automatic approach for effectively fusing Landsat 7 as well asIKONOS images. In: Proc. of the IEEE/IGARSS’02, Toronto, Canada, June 24–28,2002.

tional Journal of Remote Sensing 20 (10), 2003–2014.Zhu, G., Blumberg, D.G., 2002. Classification using ASTER data and SVM algorithms:

the case study of Beer Sheva, Israel. Remote Sensing of Environment 80 (2),233–240.