Combined Features based Spatial Composite Kernel Formation for Hyperspectral Image Classification

Transcription

1 Combned Features based Spatal Composte Kernel Formaton for Hyperspectral Image Classfcaton K.Kavtha 1, S.Arvazhagan,D.Sharmla Banu 3 Assocate Professor, Department of ECE, Mepco Schlenk Engneerng College, Tamlnadu, Inda 1 Prncpal, Mepco Schlenk Engneerng College, Tamlnadu, Inda PG Student, Department of ECE, Mepco Schlenk Engneerng College, Tamlnadu, Inda 3 ABSTRACT: Formaton of Composte Kernels wthout ncorporatng Spectral Features s nvestgated. A State of Art Spatal Feature Extracton Algorthm for makng Novel Composte Kernels s proposed for classfyng a heterogeneous classes present n Hyperspectral Images durng the unavalablty of the Spectral Features. As the classes n the hyper spectral mages have dfferent textures, textural classfcaton s entertaned. Gray Level Co-Occurrence, Run Length Feature Extracton s entaled along wth the Prncpal Component and Independent Component Analyss. As Prncpal & Independent Components have the ablty to represent the textural content of pxels, they are treated as features. Composte kernels are formed only by usng the calculated Spatal Features wthout usng Spectral Features. The proposed Composte Kernel s learned and tested by SVM wth Bnary Herarchcal Tree approach. To demonstrate the proposed algorthm, Hyper spectral Image of Indana Pnes Ste taken by AVIRIS s selected. Among the orgnal 0 bands, a subset of 150 bands s selected. Co-Occurrence and Run Length features are calculated for the selected ffty bands. The Prncple Components are calculated for other ffty bands. Independent Components are calculated for next ffty bands. Results are valdated wth ground truth and accuraces are calculated. KEY WORDS: Mult-class, Co-occurrence Features, Run Length features, PCA, ICA, Combned Features, Composte Kernels, Support Vector Machnes. I. INTRODUCTION Hyper Spectral mage classfcaton s the fastest growng technology n the felds of remote sensng and medcne. Hyper spectral mages produce spectra of several hundred wavelengths. A Hyper spectral mage has hundreds of bands; where as a mult spectral mage has 4 to 7 bands only. II. RELATED WORKS The hyperspectral mages provde more nformaton for wthn class dscrmnaton,.e., they can dscrmnate between dfferent types of rocks and dfferent types of vegetaton whle mult spectral mages can dscrmnate between rock and vegetaton [1]. Such hyperspectral mages can be used effectvely to classfy the heterogeneous classes whch are present n the mage. For classfcaton of mult-class nformaton n an mage, t s necessary to derve proper set of features and to choose proper classfer. Classfcaton of hyperspectral mages s not the trval task, as t requres so many factors to be concerned, such as () the large number of land-cover class to be handled; () Hgh number of spectral bands but low number of the avalablty of tranng samples. Ths phenomenon s known as Hughes Phenomenon [] or curse of dmensonalty. As the consequence, over fttng results..e., the classfer performs well on the tranng samples and poor on the testng samples. Ths Hughes phenomenon must be allevated; () Nonlnear spread of the data classes. To overcome these problems and to accommodate the negatve ssues assocated wth hgh dmensonal datasets, proper classfer should be selected n such a way that t has to support volumnous data, mult class data and the non-lnear dataset. Moreover proper features must be fed to the classfers to obtan the hgh accuracy rate.in spatal classfcaton, the spatal arrangement of pxels and ther contextual values, textural propertes Copyrght to IJIRSET DOI: /IJIRSET

2 are dentfed by means of the extracted features. Classfcaton can be done by usng the extracted Features. To analyse the textural propertes, the repettveness of the gray levels and the prmtves of the gray levels whch can dstngush the dfferent classes must be retreved. By ths way, the basc attrbute known as feature can be used to dentfy the regon of nterest. An extensve lterature s avalable on the pxel-level processng technque,.e., technques that assgn each pxel to one of the classes based on ts extracted features. Classfyng the pxels n the multspectral and hyperspectral mages and dentfyng ther relevant class belongngs depends on the feature extracton and classfer selecton processes. Feature extracton s the akn process whle classfyng the mages. Statstcal features such as the mean and standard devaton gves the statstcal nformaton about the pxels, whle the textural features gve the nter-relatonshp between the grey levels [3]. To fnd the textural propertes of the pxels, Co-occurrence features can be derved by usng Gray Level Co-occurrence Matrx (GLCM). Dervng the new features accordng to the applcaton s an nnovatve process. The Co-occurrence Matrx s used to extract the land-cover and land-use features of urban areas n [4]. The Co-occurrence features for ndvdual pxels n the wavelet doman are proved as a promsng technque n case of monospectral and multspectral mages. The Co-occurrence features can also be used n the transformed doman also. Both statstcal and Co-occurrence features are calculated for the decomposed wavelet subbands and are used for target detecton n [5]-[6]. The Co-occurrence features are extracted n the wavelet packet decomposton and are used to classfy the color texture mages [7]. The usage of conventonal Run Length features such as Short Run Emphass (SRE), Long Run Emphass (LRE), Gray Level Non unformty (GLN), Run Length Non unformty (RLN) and Run Percentage are explaned n [8] and are used for texture analyss. Run Length features are used to analyze the natural textures n [9]. The Domnant Run Length Feature such as Short-run Low Gray Level Emphass, Short-run Hgh Gray Level Emphass, Long-run Low Gray Level Emphass and Long-run Hgh Gray Level Emphass to extract the dscrmnant nformaton for successful classfcaton are ntroduced n [10]. From the lterature, t s evdent that the care must be shown towards the feature extracton and classfer selecton. Co-occurrence features provde the nter pxel relatonshp whch s useful for classfcaton. The runs of gray levels are useful n dentfyng the objects or classes. Prncpal and Independent Components are also the representatves of the classes also provdes reduced dmensonalty. These features are extracted and are separately used for classfcaton n the prevous classfcaton works. But n the proposed algorthm Co-occurrence features, Run Length features, Prncpal Components and Independent Components are combned together to form the Combned Features and are used for classfcaton. Such Combned Features are expected to yeld the better spatally classfed mage even whle the spectral data are not avalable. Hence n the proposed method t s decded to form the class specfc Composte Kernels usng Combned Features whch are sutable for both large spatally dstrbuted classes and for spectrally smlar classes. The usage of Prncpal and Independent Components gves the dmensonalty reducton whch leads to the reduced computatonal tme. The rest of the paper s organzed n the followng manner. Secton-III deals wth the Proposed Work followed by the Experment Desgn as Secton-IV. Secton-V s dedcated to the Results and Dscussons. Secton VI gves the Concluson about the work. III. PROPOSED WORK 3.1 Feature Extracton As a feature s the sgnfcant representatve of an mage, t can be used to dstngush one class from other. Feature extracton s the sgnfcant process whle classfyng the mages. The extracted features exhbts characterstcs of nput pxel whch the basc requrement of the classfer to make decsons about the class belongng of the pxels. The spatal features are extracted from Gray Level Co-occurrence Matrx (GLCM) and Gray Level Run Length Matrx (GLRM) and by Prncpal Component Analyss and Independent Component Analyss. After extractng such features they are to be combned to form the Combned Features. The Proposed work flow s shown n fgure Extracton of Gray Level Co-occurrence Features In Texture analyss procedure, relatve postons of pxels n mage should be consdered to get the nter-pxel relatonshp. In the Co-occurrence Matrx the dstance can be chosen from 1 to 8 and the drecton can be any one of 0 0,45 0,90 0,135 0,180 0,5 0,70 0, From the Co-occurrence matrx the features lke energy, entropy, Contrast, Homogenety, Varance, Maxmum Probablty, Inverse Dfference Moment, Cluster Tendency are extracted by usng Copyrght to IJIRSET DOI: /IJIRSET

3 the formulas shown n Table 1. Whle calculatng the features, 1 dstance and 0 0 drecton s consdered for ths experment Input mage Feature Extracton and Combned Features Formaton Formaton of Composte Kernels Tranng and Testng Phases Classfcaton usng SVM Fgure 1. Proposed Work Flow From the GLCM P(, many texture measures can be calculated. The features derved from run-length statstcs are shown n table Feature Normalzaton The scales of the eleven dfferent features vared greatly, so they were normalzed. The features need to be normalzed so that no one feature domnates the others. A feature vector was calculated for each of the mages. Form ths set of data, a sngle combned mean feature vector (µ) and a sngle combned standard devaton vector ( ) are calculated usng the combned data from all the classes n the tranng set. The normalzaton s done by usng the equaton (1)., x X I (1) x Extracton of Run Length Features For a gven mage, an element P( n the run length matrx P s defned as the number of runs wth gray level and run length j. The Run-Length Matrces are calculated n all the four drectons (0 0, 45 0, 90 0, ). From each Run- Length Matrx, the features are extracted.from the run-length matrx P(, numercal texture measures can be computed and are known as run Length Features. The features derved from run-length statstcs are shown n table. Where P s the run-length matrx, P ( s an element of the run-length matrx at the poston ( and n r s the number of runs n the mage. Most features are only functons of run length and not consderng the nformaton gven by the gray level where as n Co-occurrence features the gray levels are sgnfcantly ncluded. So, the Run Length features can able to provde extra spatal nformaton about the classes Extracton of Prncpal Components PCA s optmal n the mean square sense for data representaton. So, the Hyper spectral data are reduced from several hundreds of data channel nto few data channels. Hence the dmensonalty can also be reduced wthout losng the requred nformaton. The steps nvolved n Prncpal Component Extracton are as follows: 1. Get the mage. Calculate mean and Co-varance matrx 3. Calculate Egen vectors from the Co-varance matrx 4. Fnd Egen values 5. Order the covarance matrx by Egen value hghest and lowest 6. Egen vectors wth hghest Egen value s the Prncpal Component, whch contans sgnfcant nformaton 7. Lowest Egen value components can be dscarded The sgnfcant components known as Prncpal Components can be used as the features for classfcaton as they depct more nformaton about the mage and are good representatves of mage. x Copyrght to IJIRSET DOI: /IJIRSET

4 3.1.5 Extracton of Independent Components It s a statstcal technque whch reveals the hdden factors. As ICA separates the underlyng nformaton components of the mage data, t can be used as a feature extracton technque. ICA generates the varables whch are not only decorrelated, but also statstcally ndependent from each other. PCA makes the data uncorrelated whle ICA makes the data as ndependent as possble. Ths property s useful n dscrmnatng the dfferent classes n the mage. Table 1. Co-occurrence Features Feature Formula Remark Entropy E Measures the randomness of a gray-level P( logpj dstrbuton. j Energy Measures the number of repeated pars. En P ( Contrast j Homogenety P j Sum Mean Varance Cluster Tendency C Measures the local contrast of an mage. ( P( j Measures the local homogenety of a pxel par. H (1 j ) j Provdes the mean of the gray levels n the mage. 1 SM P ( jp( j j 1 V ( ) P( ( ) p( Tells how spread out the dstrbuton of gray level j j CT ( j ) kp( j Measures the groupng of pxels that have smlar gray level values ICA features can be used for classfcaton, as they exhbts hgh order relatonshp among mage pxels and ICA seeks to fnd the drectons of projectons of the data. Table. Run Length Features Short Run Emphass (SRE) Long Run Emphass (LRE) Feature Formula Remark M N 1 P( SRE n r j 1 j1 M N 1 LRE P(. j nr 1 j1 Gray Level Non unformty (GLN) M 1 M GLN P( n r 1 J 1 Run Length Non unformty (RLN) N M 1 RLN P( nr j1 1 Run Percentage n RPC r P( * j Measures the dstrbuton of short runs. The SRE s hghly dependent on the occurrence of short runs and s expected large for fne textures Measures dstrbuton of long runs and hghly dependent on the occurrence of long runs and s expected large for coarse structural textures Measures the smlarty of gray level values throughout the mage. The GLN s expected small f the gray level values are alke throughout the mage Measures the smlarty of the length of runs throughout the mage. The RLN s expected small f the run lengths are alke throughout the mage Measures the homogenety and the dstrbuton of runs of an mage n a specfc drecton. ICA features are hgher order uncorrelated statstcs. Whle PCA features are second order uncorrelated statstcs. Second order statstcs may nadequate to represent all the objects n the mage. To explot the nformaton from the multvarate data such as hyper spectral data, hgher order statstcs are requred. ICA features are capable of fndng the underlyng sources n such applcatons n whch PCA features fal.to dentfy the ndependency between classes the lnear transformaton s requred. If the pxels n the correspondng classes depcts zero Mutual Informaton, then they are assumed as ndependent. Ths teratve algorthm fnds the drecton for the weght vector W maxmzng Copyrght to IJIRSET DOI: /IJIRSET

5 the non-gaussanty of the projecton for the data. For the pxel vectors are denoted as X, the steps nvolved n Independent Component Extracton are as follows: 1. Choose an ntal weght vector Choose an ntal weght vector W. Let. Where the functon s the dervatve of a non-quadratc nonlnearty. 3. Let 4. If not converged, go back to 5. Repeat the process untl converges. The extracted components are Independent Components Formaton of Combned Features The extracted Co-occurrence, Run Length features, Prncple Components and Independent Components are combned as shown n table 3 and are used for classfcaton. Table 3. Lst of Combned Features 1. Combned Feature Label Formed by. Combned Feature-I CF-I Summaton of the extracted Co-occurrence Features 3. Combned Feature-II CF-II Summaton of the extracted Run Length Features 4. Combned Feature-III CF-III Combned Feature-I + Prncple Components 5. Combned Feature-IV CF-IV Combned Feature-II + Independent Components 3.. Support Vector Classfer Support Vector Machnes use the statstcal learnng theory whch maxmzes the dstance between tranng samples of two classes. Ths approach gves SVMs a hgh generalzaton capacty whch requres only a few tranng samples wthout compromsng accuracy requrement and outperforms other classfers. For a gven labelled tranng set {( ( }, s the tranng sample and s the correspondng label. For each tranng sample, the functon yelds f( ) 0 for =+1, and f < 0 for =-1. In other words, tranng samples from the two dfferent classes are separated by the hyper plane f(x) = + b=0. () Mathematcally, ths hyper plane can be found by mnmzng the cost functon as shown n equaton (7). J(w)= w = w (3) subject to the separablty constrants + b +1, for =+1 (4) or + b -1, for =-1;=1,,...,N (5) Ths specfc problem formulaton may not be useful n practce because the tranng data may not be completely separable by a hyperplane. On addng the slack constrants n equaton, t wll be modfed as ( + b) 1-, 0; =1,... N. (6) For practcal hyperspectral data, the Cost functon can be added wth equaton () J(w, ξ)= w + C (7) where C s a user-specfed, postve, regularzaton parameter and the varable ξ s a vector contanng all the slack varables =1,,...,N.Support vector Machnes performs the robust non-lnear classfcaton wth kernel trck. SVM fnds the separatng hyper plane n some feature space nducted by the kernel functon whle all the computatons are done n the orgnal space tself. For the gven tranng set, the decson functon s found by solvng the convex optmzaton problem. Copyrght to IJIRSET DOI: /IJIRSET

6 l 1 max g( ) j y y j K( x, x j ) (8) subject to 1 j1 0 C and l l 1 y 0 Where are the Lagrange co-effcent. C s a postve constant that s used to penalze the tranng errors and K s the kernel. Optmal soluton s found, by lookng to whch sde of the hyper plane t belongs by usng the decson functon f (x) mplemented by the classfer for ant test vector. l f ( x) sgn yk( x, x) b (9) 1 Composte kernels are formed by the dfferent combnaton set of GLCM and GRLM features usng RBF kernel functon. It fulflls the Mercer s Kernel condton. For an nput space X, f there s a mappng φ: X H that maps any x, z ξ X nto a Hlbert space H, then a kernel: X X R, s constructed as K(x, z) =<φ(x), φ(z)>h, where <, >H s the scalar product operator n H. Such kernel functons K satsfy the Mercer condton: the kernel matrx formed by restrctng K to any fnte subset of X s postve sem-defnte, and hence K s usually termed as Mercer kernels. xz K ( x, z) exp (10) 3.3 Formaton of Composte Kernels As the classfer parameters lke kernels have ther own mpact on accuracy, the kernel formaton and kernel selectons are also equally mportant as feature extracton. The kernel s a functon that maps the pars of vectors to real numbers or postve defnte. Kernel functon can project the vectors from the hgh dmensonal nput space nto the feature space. As feature space s comparatvely low dmensonal, the classfcaton process s easy n ths space. Choosng proper kernel amounts to choosng accurate feature space. It s the fundamental need to ncrease the accuracy. Avalable kernel s gong to be modfed to account for the cross relatonshp between the derved features. Weghted Summaton Kernel algorthm s adopted to form the Composte Kernels of postve defnte value. Ths Composte Kernels balance the Co-occurrence features, Run Length features, Prncple Component Informaton, Independent Component Informaton whch s the fundamental need for mult-class classfcaton problems. To solve hgh dmensonal data classfcaton problem, the followng Composte Kernels are used as n equatons (11) to (15). The formed composte kernels are shown n table 4. (11) (1) (13) (14) where s a postve defnte parameter whch s tuned durng the tranng process. Tunng of gves a tradeoff between the derved features and whch s useful to classfy the gven pxel. : Co-occurrence Kernel Matrx : Run Length Kernel Matrx : Co-occurrence Kernel Matrx +Prncpal Component Kernel Matrx : Run Length Kernel Matrx + Prncpal Component Kernel Matrx : Run Length Kernel Matrx+ Independent Component Kernel Matrx (15) Copyrght to IJIRSET DOI: /IJIRSET

7 : Co-occurrence Kernel Matrx + Independent Component Kernel Matrx : Composte Feature Vector-I : Composte Feature Vector-II : Composte Feature Vector-III : Composte Feature Vector-I+ Extracted Prncpal Component Vector : Composte Feature Vector-II+ Extracted Independent Component Vector Table 4. Formed Composte Kernels I Composte Kernel Name Formed by usng (x vs y) II Composte Kernel-I CK-I CF-I vs CF-II III Composte Kernel-II CK-II CF-III vs CF-II IV Composte Kernel-III CK-III CF-I vs (CF-II+PCA) V Composte Kernel-IV CK-I (CF-I+ICA) vs (CF-II+PCA) VI Composte Kernel-V CK-V CF-III vs CF-IV IV. EXPERIMENT DESIGN For evaluatng the performance of the proposed method, a sample Hyper Spectral Image whch s taken over northwest Indana s Indan pne test ste s selected. The ste s sensed by AVIRIS. The data conssts of 0 bands and each band conssts of 145 x 145 pxels. The orgnal data set contans 16 dfferent land cover classes Co-occurrence and Run Length Features are calculated for the frst ffty bands. Prncple Components are calculated for the next ffty bands. Independent Components are calculated for the other ffty bands. Frst ten Prncpal Components are used snce they contan 99.7% of the nformaton. As t s not the case for Independent Components, all the Independent Components are used. Combned features are made as shown n Table 3. Composte Kernels are formed by usng the derved Combned Features. The classfcaton detals wth tranng nformaton are shown n Table 6. The performance s valdated n the testng phase and the accuraces are quantfed and are shown n Tables 5 and 7. V. RESULTS AND DISCUSSIONS Pxels are randomly chosen from each class and ther features are used for tranng. All other pxels are tested aganst the tranng samples.the classfer produces the output, whether the pxel under test belongs to the nterested traned class or not. Thus the pxels of nterested classes are dentfed among the whole data set. Smlarly other classes are also traned. Randomly selected pxels are tested aganst the tranng samples. By ths way, classes are separated herarchcally. After dentfyng the pxels of nterested class t s labeled and ndcated by whte gray level. All other pxels are assgned black gray level. Then the pxels are dsplayed. The output s subtracted from the labeled ground truth and number of msclassfed pxels s calculated. The accuracy of each class s calculated and s shown tables 6 and 7. By observng the accuraces, t s evdent that the Composte Kernels yeld the good accuraces for all the classes than the base kernels.the Co- occurrence and Run Length features are performng equally whle they are separately used. The combnaton of Co-occurrence Features and Prncpal Components emt comparatvely good accuracy than the tradtonal Co-occurrence feature set. Run Length features along wth Independent components exhbt slght ncrement n the accuracy than the smple Run Length features. Except for few classes the combnaton of features shows ncrease n the accuracy.corn-no tll, Corn-Mn-tll, Soy-no tll, Soy-mn, Soy-Clean are the most smlar classes and ther texture varatons are also smlar. For these classes the conventonal features exhbt lower accuraces. But at least one of the proposed Composte Kernels gve better result as they provde the cross-nformaton such as Co-occurrence features, Run Length features, Prncpal Component nformaton and Independent Component nformaton.the spectral responses of the pxels belong to Soy-no tll, Soy-mn classes are smlar. For these cases, the proposed kernels evdence slghtly hgh accuraces than the Spato-Spectral Composte Kernels avalable n the Copyrght to IJIRSET DOI: /IJIRSET

8 lterature. Snce n Spato-Spectral Composte Kernels, any one of the spatal features are alone used. But n the proposed algorthm the combned features are used. So t provdes the enhanced accuracy. In coarse texture lke Alfafa, the gray level runs are longer. But fne texture lke buldngs takes the short runs. Lkewse, the runs of gray levels are varyng for each class. So, t s possble for classfyng the classes by usng Run Length features. For classes lke Soybean-mn, Alfalfa, the Run Length features have the potental for hgh accurate classfcaton. But Run Length features fal to dstngush the classes whch contan more or less same textural propertes. For dentfyng those classes, whch are outlers whle usng the tradtonal Run Length features, Prncpal Components and Independent Components are used. For the class Soy-mn, the Run Length features predomnates the Co-occurrence features snce Soy-mn class has the good prmtve nformaton for classfcaton whch can easly be captured by the Run Length features. The avalablty of tranng samples s low for the classes lke Alfalfa, Stone Steel Towers, Oats for whch, the Prncpal Components, Independent Components wth Co-occurrence features and Run length features respectvely advocates good results. But t s not the case for Grass Pasture where both Canoncal Analyss methods fal. Ths combnaton does not provde helpng hand to the classes lke Hay and Grass-Pasture mowed. Alfalfa and Grass-Pasture mowed whch are assmlated by the neghborng classes. For the class, Woods the conventonal methods and formed Composte Kernels are performng equally. But majorty of the classes cast ther vote to the Composte Kernels alone. Comparson of the measured accuraces are shown n fgure 5. Table 5.Classfcaton Accuracy Detals usng the derved Features and RBF Kernel Features C1 C C3 C4 C5 C6 C7 C8 C9 C10 C11 C1 C13 C14 C15 Average Accuracy CF-I CF-II CF-III CF- II+ICA Table 6. Classfcaton Detals Sl.No. Class Class No. No. of Tranng samples used 1 Wheat C1 16 Corn-mn C Soy-mn tll C Soy no tll C Woods C Grass/Pasture C Alfalfa C Stone Steel Tower C Bldg-grass-trees-drves C Oats C Grass-Trees C Soy-Clean C Hay-wndrowed C Corn C Corn-no tll C15 34 Copyrght to IJIRSET DOI: /IJIRSET

9 Percentage Accuracy CF-I CF-II CF-III CF-II+ICA CK-I CK-II CK-III CK-IV CK-V ISSN(Onlne) : MEASURED ACCURACY Average Accuracy Features/ Kernels Fgure 5. Measured Accuraces usng Combned Features and by Composte Kernels Even though the Indana Pnes Data set has low spatal resoluton and has mxed pxel effects, the proposed Composte Kernels yeld better results than the base kernels. As these Composte Kernels are useful for class specfc applcatons, one can choose ths for ther doman specfc applcatons too. Table 7.Classfcaton Accuracy Detals for the Proposed Composte Kernels Composte C1 C C3 C4 C5 C6 C7 C8 C9 C10 C11 C1 C13 C14 C15 Average Kernels Accuracy CK-I CK-II CK-III CK-IV CK-V VI. CONCLUSION Even though the proposed Composte Kernels produces slght ncrement n the accuracy, ths may be useful n cases where as the spectral features are not avalable. One can choose any one of the proposed kernels for class-specfc Applcatons. It s possble to develop a soft classfcaton algorthm for ths type of senstve classfcatons. For ths case of analyss, knowledge about whch class a pxel belongs to s not suffcent. If, the nformaton about, how much the pxel belongs to a partcular class s known, t wll be more useful for classfcaton. As the Soft Classfcaton extends ts applcaton to the sub-pxel levels, t can able to reduce the msclassfcatons. ACKNOWLEDGEMENT The authors are grateful to Prof. Davd. A. Landgrebe and Prof. Larry Behl for provdng the AVIRIS data set along wth the ground truth and for provdng the MultSpec package. REFERENCES [1] Davd Landgrebe, Some Fundamentals and Methods for Hyper Spectral Image Data Analyss, SPIE Photoncs, pp.1-10, Jan [] G. F. Hughes, On the mean accuracy of statstcal pattern recognzers, IEEE Transactons on Informaton Theory, IT-14(1) (1968). [3] R. M. Haralck, K. Shanmugam, I. Dnsten, Texture features for mage classfcaton. IEEE Transactons on System Man Cybernetcs, 8(6) (1973). [4] Soe Wn Mynt A Robust Texture Analyss and Classfcaton Approach for Urban Land-Use and Land-Cover Feature Dscrmnaton, n the Proceedngs of Internatonal Geocarto Conference at Hong Kong, 16(4), December 001. [5] Arvazhagan, S., Ganesan, L., Texture Classfcaton usng Wavelet Transform, Pattern Recognton Letters, 4 (),003. [6] Arvazhagan, S, Ganesan, L., DevaLakshm S, Texture Classfcaton usng Wavelet Statstcal Features, Journal of the Insttuton of Engneers (Inda), 85 (1) (003). [7] Hremath, P.S. and Shvashankar, S., Wavelet Based Features for Texture Classfcaton, GVIP Journal, 6(3) (006). [8] Galloway, Texture nformaton n Run Length Matrces, IEEE Transactons on Image Processng, 7(11) (1975) [9] Horng-Ha Loh, Ja Guu Leu, The Analyss of natural Textures usng Run Length Features, IEEE Transactons on Industral Electroncs, 35() (1988). [10] Xaoou Tang, Domnant Run- Length Method for Image Classfcaton Department of Appled Ocean Physcs and Engneerng, Woods Hole Oceanographc Insttuton, Woods Hole, Report,WHOI-97-07,June Copyrght to IJIRSET DOI: /IJIRSET