Pradhan 2014

An easy to use ArcMap based texture analysis program for extractionof ooded areas from TerraSAR-X satellite image

Biswajeet Pradhan a,n, Ulrike Hagemann b, Mahyat Shafapour Tehrany a, Nikolas Prechtel b

a Faculty of Engineering, Department of Civil Engineering, University Putra Malaysia, Serdang, Malaysiab Institute for Cartography, Faculty of Forestry, Geo and Hydro-Science, Dresden University of Technology, 01062 Dresden, Germany

a r t i c l e i n f o

Article history:Received 27 May 2013Received in revised form13 October 2013Accepted 17 October 2013Available online 6 November 2013

Keywords:Texture analysisFeature extractionRemote sensingTerraSAR-XPixel basedMalaysia

a b s t r a c t

Extraction of the ooded areas from synthetic aperture radar (SAR) and especially TerraSAR-X data is oneof the most challenging tasks in the ood management and planning. SAR data due to its high spatialresolution and its capability of all weather conditions makes a proper choice for tropical countries.Texture is considered as an effective factor in distinguishing the classes especially in SAR imagery whichrecords the backscatters that carry information of kind, direction, heterogeneity and relationship of thefeatures. This paper put forward a computer program for texture analysis for high resolution radar data.Texture analysis program is introduced and discussed using the gray-level co-occurrence matrix (GLCM).To demonstrate the ability and correctness of this program, a test subset of TerraSAR-X imagery fromTerengganu area, Malaysia was analyzed and pixel-based and object-based classication were attempted.The thematic maps derived by pixel-based method could not achieve acceptable visual interpretationand for that reason no accuracy assessment was performed on them. The overall accuracy achieved byobject-based method was 83.63% with kappa coefcient of 0.8. Results on image texture classicationshowed that the proposed program is capable for texture analysis in TerraSAR-X image and the obtainedtextural analysis resulted in high classication accuracy. The proposed texture analysis program can beused in many applications such as land use/cover (LULC) mapping, hazard studies and many otherapplications.

& 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Texture is considered as an important characteristic in imageprocessing which is useful in radar remote sensing and other eldswhere it is necessary to interpret gray value images like in themedical sector (Treitz et al., 1996; Mahmoud et al., 2011). Due to thesurface properties such as roughness, humidity and orientationevery object in SAR scene can have its own unique backscatteringproperties (Haack and Bechdol, 2000). The recognition of textureand object classication are the most challenging problems in theremotely sensed data processing (Zhang et al., 2007; Hamedianfarand Shafri, 2013). The main aim in image processing is to convertthe remote sensing (RS) imagery information into tangible informa-tion which can be understandable and possibly be used in combi-nation with other data in Geographic Information System (GIS)environment (Blaschke, 2010). Therefore precision of the data andthe texture analysis method are two main factors that have directimpact on the level of accuracy and information that can beachieved (Chica-Olmo and Abarca-Hernandez, 2000).

SAR images such as TerraSAR-X with high spatial resolution areoptimal solution for texture analysis from which meaningfultexture parameters can be deduced (DLR-Deutsches Zentrum frLuft- und Raumfahrt, 2009; Sousa et al., 2013). TerraSAR-X satelliteis Germany's rst active space borne remote sensing satellite whichhas been in orbit since 15 June 2007 (Biro et al., 2012). SAR sensorscan provide its own illumination source and it can record dataindependent of day and night time (Gibson, 2000; Buckreuss, et al.,2006). Another advantage is the ability of this data to penetrateeven cloud cover, making the image recording independent of allweather conditions (Christina Herzfeld and Zahner, 2001; Pradhanet al., 2009; Pradhan and Shae, 2009). The penetration of radarbeams depends on the wavelength, humidity and roughness of thesurface (Haack and Bechdol, 2000). SAR has a wide range ofapplications such as land use and regional/urban planning(Fugura et al., 2011; Mahmoud et al., 2011), change detection(Vidal and Moreno, 2011), disaster management (Elbialy et al.,2013; Hassaballa et al., 2013; Tehrany et al., 2013b; Pradhan andShae, 2009; Pradhan et al., 2009; Pradhan and Youssef, 2011),snow and glacier monitoring (Floricioiu et al., 2008) and sea as wellas sea ice and windmonitoring (Ren et al., 2012). Therefore, there isa need to perform the classication and extract the environmentalproperties of the ground (DellAcqua and Gamba, 2006).

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Computers & Geosciences

0098-3004/$ - see front matter & 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.cageo.2013.10.011

n Corresponding author. Tel.: 60 3 89466383.E-mail addresses: [email protected], [email protected] (B. Pradhan).

Computers & Geosciences 63 (2014) 3443

Extraction of the ooded areas, using SAR data through thetexture analysis is one of the highly demandable areas of research.Usually in tropical countries during the rainy season the area ismostly covered by clouds (Al Fugura et al., 2011; Kia et al., 2012;Pradhan, 2009; Pradhan and Youssef, 2011; Tehrany et al., 2013b;Youssef et al., 2011). In this situation optical RS cannot be used. Astexture analysis plays an important role in the visual interpretationand recognition, it is necessary to develop efcient method to extractinformation precisely (Haack and Bechdol, 2000). In the recentdevelopment of object-based methods for classication of RS data,texture is one of the most important factors in the process ofsegmentation (Ouma et al., 2008). In hazard mapping using radarimagery, the most difcult task is extracting the texture of the areawhich is not easy to perform. Some methods have been developedbut still there exist no user-friendly and simple method for textureanalysis (Kiema, 2002; Luo et al., 2012).

The current study aimed to produce an easy to use ArcMap9.2 application to solve the problem and complexity of texturerecognition in ood mapping using TerraSAR-X imagery. Also theproposed program can be used in other applications such asagriculture studies, landslide mapping, oil spill monitoring etc.At the beginning of this article mathematical background oftexture analysis will be explained. A novel application for textureanalysis using the gray-level co-occurrence matrix (GLCM) will beintroduced and discussed as well. Finally, to demonstrate theability and correctness of this application, a test subset of aTerraSAR-X image will be analyzed using simple pixel-based andobject-based classication approaches.

2. Texture analysis: a preview

Tamura et al. (1978) considered texture as a repetitive patternthat denes small areas. Haralick (1979) described it as pixels of acertain type and number that have a spatial organization eitherrandomly or with a dependency of two or more pixels. He alsosuggested that the term texture should not stand alone as it is alwaysconnected to the tone. Both require each other in a kind ofinterrelationship with one always dominating the other. Thus anarea with little gray value variation is dominated by tone; a highvariation, however, refers to texture as the dominant property.Haralick (1979) also stated that it is important to examine theproperties of single gray value pixels as well as their spatial relation-ship with each other. Therefore it is necessary to use rst and secondorder statistics. The rst order describes the properties of each grayvalue pixel separately, whereas the second order statistics examinethe relationship of pixels and thus their organization. Especially thesecond order calculation is important because texture is a quality ofnot a single pixel but of a whole area.

In a recent paper, Mahmoud et al. (2011) performed textureanalysis using TerraSAR-X data in order to enhance the land use/cover (LULC) classication near Pirna, Saxony, Germany. Theyapplied both separability and threshold (SEaTH) method to extracttextural information from the TSX image in order to assess theenhancement of the classication accuracy. Their results proved theefciency of TSX imagery and texture-based analysis in LULCmapping which leaded to acquire an overall accuracy of 95% withkappa coefcient 93%. Another study by Lee et al. (2012a), utilizedGLCM to detect the landslides on levees using SAR data. Theproposed method was applied on L-band SAR data collected fromNASA's UAVSAR of the Mississippi River levee system betweenVicksburg, MS and Clarksdale, MS, USA. All known levee landslidesin their study area could be detected with a low number of falsepositives. In a related paper by Wei et al. (2012), oil spill wasmonitored from ERS-2 SAR image using GLCM texture analysis inBohai Sea, China. Through their analysis, they discovered that

variance, contrast, dissimilarity and correlation are four texturecharacteristics suitable for classication of oil spill. So based on theliterature it can be said that texture analysis is a proper tool whichassist hazard mapping and other applications.

Various approaches are available to examine texture. Amongthose there are different structural, statistic, model-based andtransform solutions (Pant et al., 2010). The structural approachconcentrates on the hierarchical structure of texture. Hereby, therelationship between micro-texture elements (primitives) respec-tive to their spatial arrangement is of prime importance. Thestatistic approach uses non-deterministic properties. Image grayvalues distribution and relationships which are governed by theseproperties indirectly represent texture in this case. Often secondorder statistics are of importance (Lee et al., 2012b).

Another possibility to calculate texture is the model basedoption (Kim and Kang, 2007). Both generative image and stochas-tically based models are used in this approach. The image analysesare done by estimating parameters for the respective model.However, it has a high computational complexity due to the needto estimate parameters of statistic models (Pradhan, 2010). Thelast approach uses transform methods like Gabor, Fourier orwavelet transforms. In this case, the image is transformed intoanother space where the respective coordinate system representscharacteristics of the respective texture such as its frequency orsize (Materka and Strzelecki, 1998).

In thecurrent research, a statistical approachwasused for textureanalysis. The concentrationwill rely on the gray level co-occurrencematrix (GLCM) as this method was used for the implementation ofthe texture analysis tool. The co-occurrence matrix that was rstintroduced by Haralick (1979), is among the most widely used forderivation of these statistic features. The following section willintroduce the program which can be used in texture analysis ofTerraSAR-X and the efciency of this programwill be tested.

3. Methodology

The practical and theoretical aspects of texture analysis imple-mented in this study involve several steps as shown in Fig. 1.

3.1. General information on design and functionality

The texture analysis program was implemented using ArcGIS ArcObjects 9.2 and Visual Basic for Application (VBA). Fig. 2 showsthe interface of proposed program.

The program can be divided into three parts: raster denition,matrix framework creation and formula application. The graphicinterface of the program consists of one window containing threeframes. The rst frame of the starting window allows the user toselect a raster le for the texture analysis either by selecting analready opened le in ArcMap 9.2 or by searching through thedirectory. The image le will always be saved as an .img in thefolder of the original image. Depending on chosen direction andtype of analysis, an appendix will be added to the le name, thusgiving it an easy to recognize the description.

Thesecondandthirdframemakesvarious textureoptions for fourdifferent directions available to the user. The texture options whichcan be found in the second frame are divided into the three groupssuch as contrast group, orderliness group and statistics group. Toachieve these three groups, GLCM should be calculated. The GLCM,also called gray tonespatial dependencymatrix,wasrst introducedby Haralick et al. (1973). To dene this matrix, one has to picture arectangular imagewithNr rows,Nccolumnsandwithaquantizationof Ng gray levels from the tone of each single pixel. Lr{0,1,2,.,Nr}and Lc{0,1,2,.,Nc} will be the number of rows or spatial domainsand dened as the set of Ng quantied gray levels. If we create the

B. Pradhan et al. / Computers & Geosciences 63 (2014) 3443 35

matrix framework Lr Lc, then a set of pixels can be received. Theseare ordered according to row and column respectively.

Lr Lc :

0;0 0;1 0;21;0 1;1 1;22;0 2;1 2;2

Nr ;0 Nr ;1 Nr ;2

0;Nc 1;Nc

2;Nc

Nr ;Nc

26666664

37777775

1

Lr f0;1;2;:;Nrg

Lc f0;1;2;:;Ncg

Each pixel is dened as a pair of coordinates and has a gray level Gassigned to itself by a function that can be described as the imageLr Lc; I : Lr Lc-G. Now, the relative frequency of occurrence of apair of neighboring pixels or resolution cells that have a distance dbetween themcanbeexpressedas amatrixPij. Hereby the variables iand j describe respective gray levels. This matrix Pij or is called theGLCM. It is symmetric and denes distance and direction, the socalled angular relationship, of two pixel neighbors as a function(Haralick, 1979).

Fig. 2. Texture analysis program interface.

Fig. 1. Methodology ow chart.

B. Pradhan et al. / Computers & Geosciences 63 (2014) 344336

Here is the brief explanation of how the GLCM is calculated.This will lead to the formulas implemented in the texture analysistool (Hall-Beyer, 2007). As mentioned previously, the GLCM has tobe calculated prior to the implementation of the formulas. Thisrequires the following steps as shown in Fig. 3. Matrix number(1) in Fig. 3 will be a 33 subset of an image with the respectivegray values shown. The undertaken calculation will produce ahorizontal GLCM in a 33 window.

First, the matrix framework needs to be prepared. It will containthe number of occurrences of each pair of gray value combination.The pair itself consists of a neighbor pixel and a reference pixelhaving both a certain distance d away from each other and a specicspatial relationship. This can be either a horizontal, vertical ordiagonal relationship.

The offset has to be constant during the entire calculation. In theexample a distance of d1 and a horizontal relationship wasselected, thus the relationship of pixel lying next to each other isbeing examined. For instance, it can be noted that the pixelcombination (0, 1) occurs twice in the image, therefore the numbertwo will be placed at its corresponding position. After the number ofoccurrences has been determined the matrix is made symmetrical.The reason is, that both opposed directions are taken into account. Itdoes not matter whether a pair of pixels has the combination (1,0) or(0,1) as this will change when the opposite direction is considered.The symmetrical matrix V can be realized by adding the matrixframework F to its transposed matrix FT :

FFT 0 2 01 1 01 0 1

264

375

0 1 12 1 00 0 1

264

375

0 3 13 2 01 0 2

264

375 V 2

as a nal step, the symmetrical matrix V has to be normalized.Therefore each value of this matrix is divided by the sum of all valuesin V.

Pi;j Vij

N1i;j 0Vij3

Now that the GLCM has been calculated, the formulas can beapplied. As mentioned they can be divided into three groups:contrast group, orderliness group and statistics group. The pre-viously used symmetric normalized GLCM example P will be usedin the following paragraphs as a demonstration for calculation. Thesecond matrix will be containing the respective weights resultingfrom the given formula. The scalar product of these two matriceswill be the sought for result.

3.1.1. Contrast groupThe Eqs. (4)(6) belong to the contrast group. In this group the

contrast or difference in gray value between related pixels isemphasized (Eq. 4). As it can be seen in the Fig. 2 the contrastgroup has three subdivisions of contrast, similarity and homo-geneity. This calculation emphasizes higher gray value differences:

N1

i;j 0Piji j2 4

Similar to contrast the dissimilarity is calculated (Eq. 5). The imagecan display values from zero in areas of equal tone to x, where x isa positive real number depending on the radiometric resolution.For an 8 bit input image the highest value would thus be 255.

N1

i;j 0Pijji jj 5

The last part of the contrast group is homogeneity (Eq. 6).Contrary to the two aforementioned methods it uses an inversedifference approach. To avoid an error in calculations the enumeratorwas increased by one so that no zero can exist under the fraction bar:

N1

i;j 0

Pij1i j2

6

3.1.2. Orderliness groupThe second group concentrates on the orderliness of values in a

kernel. This means that it looks at their regularity (Yenugu et al.,2010). Likewise the contrast group weighted values are used.Angular second moment (ASM), and energy are the two optionsthat are clubbed together. Hereby the ASM uses its own co-occurrence matrix as weight. Thus values from one to almost zeroare possible. When close to zero a value will depend on the size ofthe searching window and direction of the GLCM. The formula 1/napplies with n N1i;j 0Vij. The value one can only be reached, if anarea displays a homogenous tone with only one gray value. Thesame applies to the energy, also referred to as uniformity, iscalculated by applying the square root to the ASM result:

ASM N1

i;j 0Pi;j

2 EnergyASM

p7

The maximum probability, short MAX is retrieved through thesimplest calculation. The highest value of the GLCM is searchedand then applied. Similar to the ASM, the result can lie betweenone and 1/n, where n is nN1i;j 0Vij. In the example the largest Pijvalue in the window is one fourth or 0.25. This might indicate aslightly higher occurrence of one or two combinations. However,no dominance is visible.

Entropy, which is another measure for order, refers to theamount of chaos or disorder in a window. Its value ranges fromzero to n, with n being dependent on the size of the searchingwindow and the direction of the neighbor pixel relationship. Inthis case n2.485. The smaller the value is, the higher the degreeof order in an image will be.

N1

i;j 0Pi;j ln Pi;j 8

3.1.3. Statistics groupThe last group that should be measured is statistics group. The

mean value in this calculation can be derived by either using thereference pixel i or j. In the case of the symmetric GLCM, this willyield an equal result because values in this matrix will appear to bemirrored on the diagonal. Therefore i and j need not be calculatedseparately. As a result values from zero to n, where n is the number of

Fig. 3. Development of GLCM (example): kernel (1), matrix framework Fij (2), symmetrical matrix Vij (3), normalized matrix Pij (4).


different gray values possible in the original image, can be calculated.The mean itself displays a value around which most likely thedominant reference pixel value can be found. In areas of high contrastthis value, however, will be further away from the reality:

N1

i;j 0i Pi;j 9

The variance is closely connected to the mean that describes thedeviation from the previous statistic element. Similar to the meancalculation, it is possible to determine both the s2i and the value s

2i .

Due to the symmetry of the matrix, however, it will not be necessary,as both results will be equal. Variance may have an advantage overthe mean as its results have a higher range of positive numbers, thusa greater stretch might be visible on output images.

The standard deviation is similar to the variance. Its onlydifference lies in another range of values as it is dened as thesquare root of the variance:

s2 N1

i;j 0Pi;j i2 Standard deviation : s

s2

p10

The last member of this statistics group is the correlation: it is anindication of how much a gray value depends on its neighbors.Results may range from zero to one, where zero points to anabsolutely uncorrelated image subset and one refers to fully corre-lated areas. It can be seen that an error may be received as a result, ifthe variance of an area is zero because the variance can be found inthe enumerator of the formula and thus a division by zero can occur.This might only happen if only one gray value exists in the respectivearea. This subset would be fully correlated. Thus, when programmingthe correlation the result can be set to one in this case:

N1

i;j 0Pi;j

ijjjs2 is2 j

q

264

375 -simplified :

N1

i;j 0Pi;j

ijs2

11

3.1.4. Program usabilityCertain steps have to be undertaken in a program, in order to

make it work smoothly and user friendly. Although they are notnecessary themselves for the main program, but they can be usefulfor the user as they give further options and also they can givewarning of errors. Among those it can be mentioned to thepossibility of opening the les that have already been opened inArcMap 9.2, to be informed about existing les of the same name.Also it can give the opportunity to overwrite these les and tocreate multiband images for four directions of the same textureoption. Also using le selection and drop down menu the mistakeof accidentally selecting a non-raster le can be prevented. Thesefunctions are also added to the main program.

3.2. Analysis

3.2.1. Data and study areaThe study is carried out in Terengganu which is situated in

north-eastern part of Peninsular Malaysia, and is bordered in thenorthwest by Kelantan, the southwest by Pahang, and the east bythe South China Sea (Zaleha et al., 2006). The data used in thiswork was recorded by TerraSAR-X satellite, short TSX-1, from 27thNovember 2009. Data was single look, stripmap modus, with threemeters spatial resolution and HH polarization. TerraSAR-X dataused with radiometric resolution of 16 bit thus providing 65,536different gray values.

3.2.2. Pre-processingIn order to guarantee good results, the images have to be pre-

processed (Albinet et al., 2012). Therefore the following steps are

followed. First, the speckle effect needs to be removed (Idreeset al., 2013). ERDAS IMAGINE provides a few lters which willsuppress and smooth out this effect (Haack and Bechdol, 2000).However, not all of them are equally suitable. Dong et al. (2000)discussed this issue, comparing different lters regarding theirmean, edge and textural information preservation as well as theirreduction of the standard deviation. They also indicated that aspeckle reducing lter should not distort and degrade the inherenttexture if it is intended for texture preservation. After evaluatingthe Lee, Kuan, Frost, mean, median and edge-sharpening lter theyconcluded, that the median lter was not suitable as a specklelter for SAR data as it strongly distorts the texture.

Comparison has been done between the outputs of these ltersusing signal to noise ratio (SNR). Visual interpretation concludedthat frost lter performed better than the rest in this study. TheFrost lter displays this area very well, with low noise levels. Forthis purpose, 55 window Frost lter was used to remove thespeckle and the results showed features are still visible withoutappearing blur. Moreover, line and point features are clearly visibleand the texture context seems to have been least altered.

In the next step the radiometric resolution has to be rescaled fromunsigned 16 bit to unsigned 8 bit which means that the number ofgray values available has to be downscaled from 65,536 to 256. Thereason therefore is an incompatibility of a method used withArcObjects. It does not matter which method is used, information willalways be lost when an image is rescaled (Zan et al., 2008). Thequestion that should now be answered is: which method does theleast damage to the original texture information of the image? Threedifferent methods using ERDAS IMAGINE were used and analyzed.Finally the method which selected was the one that can be found asrescale option through ERDAS Interpreter menu under the optionutilities. The reason for this selection is it has neither the problem of astrongly altered contrast situation nor does it provide oating grayvalues.

Finally, suitable subset has to be found. Fig. 4 shows theproper scene. The Terengganu river can be recognized as well asa mountainous area and different agricultural elds with differenttypes of crops. Also rectangular structure of different buildings andthe linear, dark structure of streets can be seen.

Fig. 4. Subset of area of the Terengganu River, Malaysia.


Fig. 5. Original image (upper left) and contrast group results: contrast (upper right), dissimilarity (lower left), homogeneity (lower right).

Fig. 6. Orderliness group results: ASM (upper left), energy (upper right), entropy (lower left), MAX (lower right).


3.2.3. Texture analysis and classicationAfter all these steps have been undertaken, the texture analysis

could be applied to the subset image using the proposed program. Inrecent years, the object-based classication methods proved to bemore efcient than pixel based due to the spatial information that isused in these methods (Al Fugara et al., 2009; Blaschke et al., 2008;Mahmoud et al., 2011). Results of the texture analysis were used inpixel-based classication and four classes could be recognized by theERDAS program: water body, settlement, agriculture/elds andforest. Also advanced object-based classication method was appliedon the outputs of the texture analysis and nally compared with the

pixel-based result. As we know segmentation is the basis of theobject-based classication that divides the image into the homo-geneous objects and classies these objects based on spectral, spatial,textural, relational and contextual information (Petropoulos et al.,2012). These regions are homogenous in some way but also differfrom their adjacent regions (Morris et al., 1986). Thus this segmenta-tion corresponds in a way with the human perception of areas.

For object-based classication Deniens eCognition 7.0 wasused which is very popular in optical and radar remote sensing(Tehrany et al., 2013a). However, before the actual segmentationprocess it was necessary to combine all texture results which

Fig. 7. Statistics group result: mean (upper left), variance (upper right), Standard deviation (lower left), correlation (lower right).

Fig. 8. Exemplary result of a pixel based classication with ERDAS Imagine.


achieved from texture analysis into one image. This was doneusing Composite Bands of ArcMaps Data Management Tools. Thiscreated an image le with 44 bands. After a recalculation of itsstatistics and the assignment of the no data value 9999, theimage could be used for segmentation. Scale and color values wereselected, depending on the properties of the image used. Herebythe resolution of the image object level is dened by the scaleparameter. Thus low values indicate that smaller objects will becreated. The second parameter, color, signies the importance ofimage color homogeneity in the segmentation process. For thiswork, scale received a parameter value of ten and 0.9 was assignedas the color parameter. After the execution of the segmentationprocess an image containing segmentation polygons was calcu-lated and returned. Nearest neighbor was chosen as the classica-tion method. Color and mutual embedding were both taken intoconsideration. Training areas could be dened by assigning certainsegments to either a positive or negative predened output class.This pre-denition had to be done by the user and consisted of aname and a color for the later class. For example, the rstclassication cycle contained the positive output class water (blue)and the negative output class land (green). This way an evaluationof the texture analysis program and its results can be conducted.

4. Results and discussion

The results of the structural texture analysis are as follows. Theimage contains four bands to display the four possible directions.Small part of the study area has been chosen to represent here, in

order to show the impact of each group of texture analysis on datamore clearly. As can be seen in Fig. 5 results of the contrast groupproduced concordant results. Settlement areas had high contrastvalues, and thus contained low homogeneity values, whereaswater bodies and ooded areas displayed a low contrast and thushigh homogeneity.

The surrounding area displays lower contrast values. Also theresults of orderliness group and statistics group can be seen inFigs. 6 and 7 respectively. Classication scheme was applied usingthe texture analysis results to examine the efciency of twoclassiers and to assess the impacts of the texture informationon the precision of the results of each classication. The result ofthe pixel-based classication is shown in Fig. 8.

Visual interpretation of the thematic map derived by ERDASimagery has illustrated the weakness of pixel-based to extract thefeature classes. Due to the high oat value variation in thedifferent images a pixel based approach is not advisable. Thiswould simply yield inhomogeneous areas with a high amount ofnoise as well as misclassication. As can be seen in Fig. 8, waterwas not recognized by the program. Although training areas wereused and a supervised classication was conducted, it was inter-preted as forest or agriculture area. Also clearly visible are theinhomogeneous areas where forest or agricultural areas would besuspected. Therefore a pattern recognition approach using seg-mentation was examined. Fig. 9 shows the image before applyingthe segmentation. It was possible to differentiate water from theland. Also a settlement can be recognized. Fig. 10 shows a part ofthe segmented image containing the biggest settlement as well aselds and ooded area.

Fig. 9. Six layer false color representation of the texture band composite in eCognition with possible elements for later classication (rotated 901 clockwise). (Forinterpretation of the references to color in this gure legend, the reader is referred to the web version of this article.).

Fig. 10. Result of the multiresolution segmentation process.


As can be seen in the Fig. 10, different classes could beseparated very well through the segmentation and it shows theefciency of the object-based classication and the strangeness ofeCognition software in precise segmentation. Fig. 11 shows thethematic map acquired by object-based method and ve landcover classication results were obtained.

As can be seen in Fig. 11, the thematic map has acceptable andrepresentable appearance which proves the efciency of theobject-based classication. In some areas some misclassicationsare obvious but accuracy assessment should be done because it isnot proper to do any judgments without considering and evaluat-ing the statistics. For that reason, the level of correctness for theresults of classications was evaluated by accuracy assessmentthrough confusion matrix (Foody, 2002; Magee, 2011). Table 1shows the results of the accuracy assessment. The results indicatedthat the proposed object-based method produced an acceptableproducer and user accuracies for all the classes except the class ofsettlement and wood. The reason could be related to the mixtureof the spectral information of two classes of settlement and wood,

so the probability of having errors is very high. Also due to spectralsimilarity of the class of settlement and wood and the class ofwood, their accuracies are lower than other classes. This impliesthat it was difcult for the program to differentiate between thesetwo classes.

5. Conclusion and recommendation

In this paper, we have developed an easy to use ArcMapprogram which is made in visual basic environment and it is ableto perform the texture analysis of features using TerraSAR-Ximagery. Secondly, ERDAS and eCognition software were used forpixel-based and object-based classication respectively in order toexamine the precision of both methods in extracting the features.Results showed that pixel-based approach was not that successful,whereas object-based approach showed very good results. Object-based by 83.63% overall accuracy and 0.8 kappa coefcient provedits efciency in detecting the features over traditional pixel-basedmethod. Results on image texture classication showed that theproposed program is capable in the analysis of TerraSAR-Ximagery. Hence the developed program can be used in many earthobservation applications such as LULC mapping, change detection,and hazard studies. The program could differentiate between theobjects which lead to enhance their results. The program itselfperformed well on the task but the computation process took littlelonger time. If the algorithm is enhanced that might improve theprocessing time. However, the most time consuming aspect of thisprogram is the creation of pixel blocks, therefore improvement ofalgorithm does not have signicant impact. Thus to really reducethe runtime it is advisable to use smaller signicant subsets of ascene. The currently available version is a robustly running betaversion. However, some features have not yet been implemented.The two most important features are, are the use of different sizedkernels in order to assess a greater area of texture and thequestion of the no-data value handling. Further, improvementsin usability and user options might be implemented. Also usingdata fusion can improve the accuracy of the texture analysis.

Acknowledgments

The German Aerospace Center (DLR) provided Terra-SAR-X dataunder the Science proposal ID: HYD0326. Thanks to Thomas Hah-mann for his valuable inputs on the processing of Terra-SAR-X data.

References

Al Fugura, A., Billa, L., Pradhan, B., Mohamed, T.A., Rawashdeh, S., 2011. Coupling ofhydrodynamic model and aerial photogrammetry-derived digital surface modelfor ood simulation scenarios using GIS: Kuala Lumpur ood. Malaysia. DisasterAdv. 4 (4), 2028.Fig. 11. Result derived by object-based method.

Table 1Object-based classication accuracy assessment.

Settlement Water Settlement and wood Wood Woodless Sum User accuracy %

Settlement 46 1 2 0 0 49 94Water 0 51 0 0 0 0 100Settlement and wood 0 2 29 7 12 50 58Wood 1 4 4 33 1 43 77Woodless 0 2 0 1 30 33 91Sum 50 37 51 50 50 238Producer accuracy % 98 85 83 80 70

Overall accuracy 83.6%Kappa coefcient 0.8


Al Fugara, A.M., Pradhan, B., Mohamed, T.A., 2009. Improvement of land-useclassication using object-oriented and fuzzy logic approach. Appl. Geomat. 1(4), 111120.

Albinet, C., Borderies, P., Fabre, S., 2012. Radar and optical parallel modelling offorest remote sensing data. Int. J. Remote Sens. Appl. 2, 4.

Biro, K., Pradhan, B., Sulieman, H., Buchroithner, M., 2012. Evaluation of TerraSAR-Xdata for land use/land cover analysis using object-oriented classicationapproach in the African Sahel Area, Sudan. J. Indian Soc. Remote Sens. , http://dx.doi.org/10.1007/s12524-012-0230-7.

Blaschke, T., Lang, S., Hay, G.J. (Eds.), 2008. Object-Based Image Analysis SpatialConcepts for Knowledge-Driven Remote Sensing Applications, rst ed. Springer,New York.

Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS J.Photogramm. Remote Sens. 65 (1), 216.

Buckreuss, S., Mhlbauer, P., Mittermayer, J., Balzer, W., Werninghaus, R., 2006. TheTerraSAR-X Ground Segment. In: Proceedings of the European Conference onSynthetic Aperture Radar (EUSAR). Dresden, Germany.

Chica-Olmo, M., Abarca-Hernandez, F., 2000. Computing geostatistical imagetexture for remotely sensed data classication. Comput. Geosci. 26 (4),373383.

Christina Herzfeld, U., Zahner, O., 2001. A connectionist-geostatistical approach toautomated image classication, applied to the analysis of crevasse patterns insurging ice. Comput. Geosci. 27 (5), 499512.

DellAcqua, F., Gamba, P., 2006. Discriminating urban environments using multi-scale texture and multiple SAR images. Int. J. Remote. Sens. 27 (18), 37973812.

DLR-Deutsches Zentrum fr Luft- und Raumfahrt, 2009. DLR Portal- Anwendungenund Nutzung. DLR Portal. [Online] Deutsches Zentrum fr Luft- und Raumfahrt,20. March 2009. (Zitat vom: 21. February 2010) http://www.dlr.de/desktopdefault.aspx/tabid-4219/8885_read-16087/.

Dong, Y., Milne, A.K., Forster, B.C., 2000. A review of SAR speckle lters: texturerestoration and preservation. In: Proceedings of the Geoscience and RemoteSensing Symposium 2000 (IGARSS 2000), IEEE 2000 International, vol. 2,pp. 633635.

Elbialy, S., Mahmoud, A., Pradhan, B., Buchroithner, M., 2013. Application ofspaceborne synthetic aperture radar data for extraction of soil moisture andits use in hydrological modelling at Gottleuba Catchment, Saxony, Germany. J.Flood Risk Manag. , http://dx.doi.org/10.1111/jfr3.12037.

Floricioiu, D., Eineder, M., Rott, H., Nagler, T., 2008. Velocities of major outletglaciers of the Patagonia Iceeld observed by TerraSAR-X. In: Proceedings of theGeoscience and Remote Sensing Symposium 2008 (IGARSS 2008). IEEE Inter-national, vol. 4, pp. IV347.

Foody, G.M., 2002. Status of land cover classication accuracy assessment. RemoteSens. Environ. 80 (1), 185201.

Fugura, A., Billa, L., Pradhan, B., 2011. Semi-automated procedures for shorelineextraction using single RADARSAT-1 SAR image. Estuar., Coast. Shelf Sci. 95 (4),395400.

Gibson, P., 2000. Introductory remote sensing principals and concepts. Taylor andFrancis, New York.

Haack, B., Bechdol, M., 2000. Integrating multisensor data and RADAR texturemeasures for land cover mapping. Comput. Geosci. 26 (4), 411421.

Hall-Beyer, M. GLCM Texture Tutorial, 2007. University of Calgary. (Online)University of Calgary, 21. February 2007. (Zitat vom: 2. February 2010)http://www.fp.ucalgary.ca/mhallbey/tutorial.htm.

Hamedianfar, A., Shafri, H.Z., 2013. Development of fuzzy rule-based parameters forurban object-oriented classication using very high resolution imagery. Geo-carto. Int. , http://dx.doi.org/10.1080/10106049.2012.760006.

Haralick, R.M., Shanmugam, K., Dinstein, I.H., 1973. Textural features for imageclassication. IEEE Trans. Syst. Man. Cybern. 6, 610621.

Haralick, R.M., 1979. Statistical and structural approaches to texture. Proc. IEEE 67(5), 786804.

Hassaballa, A.A., Althuwaynee, O.F., Pradhan, B., 2013. Extraction of soil moisturefrom RADARSAT-1 and its role in the formation of the 6 December 2008landslide at Bukit Antarabangsa, Kuala Lumpur. Arab. J. Geosci., 110, http://dx.doi.org/10.1007/s12517-013-0990-6.

Idrees, M.O., Saeidi, V., Yusuf, Y.A., Pradhan, B., 2013. Advanced differentialinterferometry SAR techniques for deformation monitoring: A review onsensors and recent research development. Geocarto. Int. , http://dx.doi.org/10.1080/10106049.2013.807305.

Kia M.B., Pirasteh S., Pradhan B., Mahmud A.M., Sulaiman W.N.A., Moraddi A., 2012.An articial neural network model for ood simulation using GIS: Johor riverbasin, Malaysia. Environ. Earth Sci. 67 (1), 251264, http://dx.doi.org/10.1007/s12665-011-1504-z.

Kiema, J., 2002. Texture analysis and data fusion in the extraction of topographicobjects from satellite imagery. Int. J. Remote Sens. 23 (4), 767776.

Kim, S.C., Kang, T.J., 2007. Texture classication and segmentation using waveletpacket frame and Gaussian mixture model. Pattern Recognit. 40 (4), 12071221.

Lee, M.A., Aanstoos, J.V., Bruce, L.M., Prasad, S., 2012a. Application of omni-directional texture analysis to SAR images for levee landslide detection. In:Proceedings of 2012 IEEE International Geoscience and Remote SensingSymposium (IGARSS), IEEE, pp. 18051808.

Lee, S.J., Serre, M.L., van Donkelaar, A., Martin, R.V., Burnett, R.T., Jerrett, M., 2012b.Comparison of geostatistical interpolation and remote sensing techniques forestimating long-term exposure to ambient PM2. 5 Concentrations across theContinental United States. Environ. Health Perspect. 120 (12), 17271732.

Luo, Y., Zhao, S., Zhou, H., Wang, A., He, K., Tan, L., 2012. A novel classicationmethod based on texture analysis using high-resolution SAR and optical data.In: Proceedings of the 2012 Second International Workshop on the EarthObservation and Remote Sensing Applications (EORSA), IEEE, pp. 186190.

Magee, K.S., 2011. Segmentation, object-oriented applications for remote sensingland cover and land use classication (Ph.D. dissertation). University ofCincinnati, USA. (131pp).

Mahmoud, A., Elbialy, S., Pradhan, B., Buchroithner, M., 2011. Field-based landcoverclassication using TerraSAR-X texture analysis. Adv. Space Res. 48 (5),799805.

Materka, A. Strzelecki, M., 1998. Texture Analyisis Methods A Review.Morris, O.J., Lee, M.D.J., Constantinides, A.G., 1986. Graph theory for image analysis:

an approach based on the shortest spanning tree. Communications, Radar andSignal Processing. IEE Proceedings, F 133 (2), 146152.

Ouma, Y.O., Josaphat, S.S., Tateishi, R., 2008. Multiscale remote sensing datasegmentation and post-segmentation change detection based on logical mod-eling: theoretical exposition and experimental results for forestland coverchange analysis. Comput. Geosci. 34 (7), 715737.

Pant, T., Singh, D., Srivastava, T., 2010. Advanced fractal approach for unsupervisedclassication of SAR images. Adv. Space Res. 45 (11), 13381349.

Petropoulos, G.P., Kalaitzidis, C., Prasad Vadrevu, K., 2012. Support vector machinesand object-based classication for obtaining land-use/cover cartography fromHyperion hyperspectral imagery. Comput. Geosci. 41, 99107.

Pradhan, B., 2009. Flood susceptible mapping and risk area estimation using logisticregression, GIS and remote sensing. J. Spat. Hydrol. 9 (2), 118.

Pradhan, B., Shae, M., 2009. Flood hazard assessment for cloud prone rainy areasin a typical tropical environment. Disaster Adv. 2 (2), 715.

Pradhan, B., Shae, M., Pirasteh, S., 2009. Maximum ood prone area mappingusing RADARSAT images and GIS: Kelantan river basin. Int. J. Geoinf. 5 (2),1123.

Pradhan, B., 2010. Remote sensing and GIS-based landslide hazard analysis andcross-validation using multivariate logistic regression model on three test areasin Malaysia. Adv. Space Res. 45 (10), 12441256.

Pradhan, B., Youssef, A.M., 2011. A100-year maximum oood susceptibility map-ping using integrated hydrological and hydrodynamic models: Kelantan RiverCorridor. Malaysia. J. Flood Risk Manag. 4 (3), 189202, http://dx.doi.org/10.1111/j.1753-318X.2011.01103.x.

Ren, Y., Lehner, S., Brusch, S., Li, X., He, M., 2012. An algorithm for the retrieval of seasurface wind elds using X-band TerraSAR-X data. Int. J. Remote Sens. 33 (23),73107336.

Sousa, J.J., Magalhes, L.M., Ruiz, A.M., A.M.R., Cardoso, G., 2013. The viStaMPS toolfor visualization and manipulation of time series interferometric results, seriesinterferometric results. Comput. Geosci. 52, 409421, http://dx.doi.org/10.1016/j.cageo.2012.11.012.

Tamura, H., Mori, S., Yamawaki, T., 1978. Textural features corresponding to visualperception. IEEE Trans. Syst. Man Cybern. 8 (6), 460473.

Tehrany, M.S., Pradhan, B., Jebu, M.N., 2013a. A comparative assessment betweenobject and pixel-based classication approaches for land use/land covermapping using SPOT 5 imagery. Geocarto. Int., http://dx.doi.org/10.1080/10106049.2013.768300.

Tehrany, M.S., Pradhan, B., Jebur, M.N., 2013b. Spatial prediction of ood susceptibleareas using rule based decision tree (DT) and ensemble bivariate andmultivariate statistical models. J. Hydrol. 504, 6979, http://dx.doi.org/10.1016/j.jhydrol.2013.09.034.

Treitz, P.M., Soulis, E.D., Howarth, P.J., Kouwen, N., 1996. Texture processing ofsynthetic aperture radar data using second-order spatial statistics. Comput.Geosci. 22 (1), 2734.

Vidal, A., Moreno, M.R., 2011. Change detection of isolated housing using a newhybrid approach based on object classication with optical and TerraSAR-Xdata. Int. J. Remote Sens. 32 (24), 96219635.

Wei, L., Hu, Z., Guo, M., Jiang, M., Zhang, S., 2012. Texture feature analysis in oil spillmonitoring by SAR image. In: Proceedings of IEEE 20th International Con-ference on Geoinformatics (GEOINFORMATICS), 2012, pp. 16.

Yenugu, M.M., Marfurt, K.J., Matson, S., 2010. Seismic texture analysis for reservoirprediction and characterization. Lead. Edge 29 (9), 11161121.

Youssef, A., Pradhan, B., Hassan, A.M., 2011. Flash ood risk estimation along the St.Katherine road, southern Sinai, Egypt using GIS based morphometry andsatellite imagery. Environ. Earth Sci. 62, 611623.

Zaleha, K., Roswati, M.A., Iwasaki, N., 2006. Distribution of some species ofharpacticoid copepods in east coast of Peninsular Malaysia. Coast. Mar. Sci.30 (1), 141145.

Zan, F.D., Guarnieri, A.M., Rocca, F., Tebaldini, S., 2008. Sentinel-1 radar interfero-metry applications. In: Proceedings of 2008 7th European Conference onSynthetic Aperture Radar (EUSAR), VDE, 2008, pp. 14.

Zhang, J., Marszaek, M., Lazebnik, S., Schmid, C., 2007. Local features and kernelsfor classication of texture and object categories: a comprehensive study. Int. J.Comput. Vis. 73 (2), 213238.


An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite imageIntroductionTexture analysis: a previewMethodologyGeneral information on design and functionalityContrast groupOrderliness groupStatistics groupProgram usability

AnalysisData and study areaPre-processingTexture analysis and classification

Results and discussionConclusion and recommendationAcknowledgmentsReferences

Pradhan 2014

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