Texture-based classification of cloud and ice-cap surface features

Transcription

1 Aals of Glaciology Iteratioal Glaciological Society Texture-based classificatio of cloud ad ice-cap surface features W. G. REES AND I-I LIN Scott Polar Research Istitute, Uiversity of Cambridge, Lesfield Road, Cambridge CB2 IER, U.K. ABSTRACT. It is well kow that the iterpretatio of high resolutio «1 m) visible ad ear ifrared (e.g. Ladsat) imagery oflarge ice masses is hidered by the uiform reflectivity of sow, ice ad cloud surfaces. Such iterpretatio is at preset largely performed maually, but there is a good prospect that it could be automated by the icorporatio of image texture. This paper describes prelimiary work towards the idetificatio of the most appropriate texture techique, or combiatio of techiques, ad assesses the likely performace of such methods. Differet textures are idetified with differet types of surface cover, ad the use of these differeces to classify images is ivestigated. Specifically, we compare a traditioal texture measure, the Grey Level Co-occurrece Matrix (GLCM), with a modificatio of a relatively ew techique, fractioal Browia motio (FBM). These two methods are applied to three Ladsat MSS images of the Nordaustladet ice cap, Svalbard. The classificatio accuracy, computatio time ad memory required, advatages ad limitatios of the two methods are compared. The GLCM techique appears to be able to distiguish three groups of image classes, amely dry sow, wet sow, ad melt features, ablatio areas or cloud cover. The FBM techique is computatioally more efficiet, ad though it performs i geeral less well tha the GLCM techique it gives better discrimiatio of cloud cover. INTRODUCTION The potetial of Ladsat-type images with high spatial resolutio «1 m ) to reveal surface features o terrestrial ice masses has log bee recogised (e.g. Swithibak, 1988). Previous work has mostly bee directed towards spectral classificatio combied with visual iterpretatio (e.g. Orheim ad Luchitta, 1987; Dowdeswell ad McItyre, 1987). However, such aalysis is hidered by the uiformity of reflectace of sow ad cloud surfaces, which gives rise to a very limited rage of toal variatio i such images. Texture aalysis, which quatifies the spatial variatio of image toe, has the potetial to provide importat complemetary data (Welch ad others, 1989, 199; Key, 199) ad has already show the value of texture aalysis for idetifyig the presece (this is particularly difficult over sow ad ice masses), ad perhaps type, of cloud cover. It is ot surprisig that there should be cosiderable iterest i the use of texture methods for discrimiatig betwee these image classes. Apart from the egative reaso that it is difficult to effect a discrimiatio usig oly radiometric data, there is a physical justificatio for expectig the use of image texture to succeed, sice bare ice, sow ad cloud have distict morphologies. TECHNIQUES FOR THE QUANTIFICATION OF IMAGE TEXTURE It is ot easy to give simple defiitio of image texture (Mather, 1987; Rees, 199), sice i priciple it describes all spatial depedecies of the image. I practice, a fiite umber of texture parameters is defied. Typically these iclude the cocepts of image roughess, homogeeity, liearity, cotrast etc. However, the defiitio of these parameters ofte ivolves a subjective elemet so that it is possible to defie a umber of parameters, givig differet umerical values, all of which are iteded to quatify the same texture quality. May differet texture measures have bee developed; the most widely used method is based o the Grey Level Co-occurrece Matrix (GLCM) (Haralick ad others, 1973; Haralick, 1979). I this research, we compare the performace of this method with that of a ew techique, based o the cocept of fractals (Feder, 1987; Li ad Rees, 1992), i classifyig three Ladsat multispectral scaer (MSS) images of the Nordaustladet ice cap, Svalbard. Grey Level Co-occurrece Matrix The GLCM techique has bee described i detail by Haralick ad others (1973). Here we provide a summary of the importat aspects. Texture properties are defied for a two-dimesioal widow i the image, ad calculated as various properties of the GLCM measured withi the widow. If the image cotais grey levels (e.g. = 256 for 8-bit data), the GLCM is a by square matrix S i which the elemet Sij(V) is the frequecy with which grey level i is separated from grey level j by a vector v. The matrix S is thus a fuctio of the chose displacemet vector v. 25

2 Rees ad Li: Texture-based classificatio of cloud ad ice-cap features May texture parameters ca be extracted from the GLCM. The three which are studied i this work are the agular secod momet (ASM), the cotrast (CON), ad the dissimilarity (DIS) ASM= LLS;; i=l ;=1 CON = LL(i - j)2s;j ;=1 j=l DIS = usig Ladsat imagery that the surface topography of a ice cap may be described as a surface of dimesio Sice, for the purposes of the preset paper, the cocept offracitioal dimesioality merely provides a coveiet framework i which to describe image texture, we refer the reader to the refereces give above, ad other refereces cotaied therei, ad preset here oly the mai parameters. The seimvariogram 'Y(v) of a image is defied, followig (e.g. ) Curra ( 1988), as LL li - jlsi; 'Y(v) ;=1 ;=1 The agular secod momet is essetially a measure of the homogeeity of the image, sice it reaches its maximum possible value if the pixels withi the image, or withi the widow uder study, all have the same (arbitrary) grey level. It achieves its miimum possible value if the grey levels are uiformly distributed over the possible rage of values, ad ucorelated o the scale v used i the aalysis. The cotrast ad dissimilarity, o the other had, measure the tedecy of eighbourig pixels to have differet values of grey level, ad both become zero for homogeeous images. The cotrast parameter assigs a greater weight to large differeces i grey level tha does the dissimilarity parameter. The agular secod momet is uaffected the image calibratio, but the other two paramters are chaged if the calibratio is chaged. If the calibratio is chaged such that a grey level of x becomes a grey level kx, the cotrast icreases by a factor of k 2 ad the dissimiliarity by a factor of k. I this research, the widow size is chose as 24 by 36 pixels (correspodig to a area about 2 km by 2 km o the groud), ad the vector displacemet v is chose as (1,), so that oly variatios i the x-directio of the image (vertically orieted features ) are measured. This choice of v is clearly somewhat arbitrary, though it is quite commo i the use of the GLCM for the study of image texture. I priciple, oe should use all possible values of v withi the image widow. However, the amout of computatio required to geerate the GLCMs ad the to extract the texture paramters would be extremely large, ad our result show that takig oly Ivl = 1 gives good discrimiatio of several differet image classes. The restrictio to vertically orieted features poses a potetial problem, though for features which are likely to be statistically isotropic such as those studied i the preset work the problem is likely to be more apparet tha real. Agai, the restrictio ca be justified pragmatically o the grouds that the approach yields geerally good discrimiatio. Fra ctioal Browia Motio The fractioal Browia motio (FBM) method is based o the cocept of fractals, which are geometric costructios with fractioal dimesioality (as distict from the more covetioal itegral dimesioality of Euclidea geometry, i which lies have dimesio I, surfaces have dimesio 2, ad volumes have dimesio 3). The applicatio of this idea to the study of ice sheets has bee discussed i detail by Rees (1992), who showed = < [I(x) - I(x + v)]2 > 2 where I(x) is the grey level of the pixel whose coordiate is x, ad the average represeted by <> is performed over all the pixels lyig withi a widow of the image. The widows were chose to be ide tical to those used for the GLCM aalysis. A semivariogram is formed by evaluatig 'Y(v) for vector displacemets v alog a oedimesioal trasect. For surfaces which display true fractioal Browia motio, where D is a costat kow as the fractal dimesio. For real surfaces this relatioship is expected to break dow for some miimum ad some maximum value of v. The techique we adopt, described i greater detail i Li ad Rees (1992), is to plot the 'Y( v) relatioship as log b) agaist log (v) ad to use a iterative procedure to defie the optimum straight lie through the poits, ad the slope of this lie, from which D ca be determied. We also defie a parameter which we call the "shift", which is the extrapolated value oflog b) whe log (v) = o. It thus correspods to the image variace for a sigle pixel. DATA SET We have compared the performace of the GLCM ad FBM texture methods by usig them to study three Ladsat MSS images of Nordaustladet, Svalbard, for which image classificatios were kow. Three images were eeded to give examples of all the image classes (discussed below) which we wished to study. Image I is bad I (.5 to.6 J1.m) of the Ladsat I image from path 233, row 2, acquired o 25 March This image (which is illustrated i Li ad Rees, 1992) shows a dry sow surface. Image 2 is bad 4 (.8 to 1. J1.m ) of the Ladsat 2 image from path 233, row 2, acquired o 3 July This image is partly covered by thi cirrus cloud (Fig. I). Image 3 is bad I of the Ladsat 2 image from path 234, row 2, acquired o I August This image shows various melt features, as well as areas of wet sow (Fig. 2). It would have bee better to be able to use the same spectral bad throughout our aalysis, but (a commo problem with 1 or 12 year old data o magetic tape) some chaels had become corrupted. However, the very high correlatio betwee visible ad ear ifrared bads i images of sow ad cloud meas that this is ulikely to have itroduced ay sigificat errors ito our study of image texture. A very simple calibratio was performed o each image, by scalig the 251

3 Rees ad Li: Texture-based classificatio of cloud ad ice-cap features Fig. 1. Image 2 (Ladsat 2, Bad 4, July 1876). The image shows a area approximately 185 km square. Fig. 2. Image 3 ( Ladsat 2, Bad 1, August 1981). The image shows a area approximatery 185 km square. total rage of grey levels to the rage to 63 (i.e. rescalig them to 6-bit data). 164 widows of 24 by 36 pixels were defied withi three extracts of these images, show i Figures 1 ad 2 ad selected to cotai areas idetified as dry sow, wet sow, melt areas (distiguishable from wet sow by the appearace of melt streams ad melt pods), ablatio areas, ad cloud cover. These idetificatios were performed by visual iterpretatio ad by compariso with Dowdeswell 91984) ad G.S. Hamilto (persoal commuicatio). The texture parameters defied above were calculated for each widow. RESULTS GLCM techique Values of the agular secod momet (ASM), cotrast (CON) ad dissimilarity (DIS) were calculated for a total N u , o ee ~o III ERI i'i!p., If' ++ ~~ A "'~ 'dal... [4 A~... - ~l>-aorq.( A AtP., ~o PC, Fig. 3. Pricipal compoets derived from the GLCM aarysis. The circles represet dry sow, the squares wet sow, the triagles melt areas, the lo.<;eges ablatio areas ad the crosses cloud. 7 of 1643 widows i the three images, represetig all five image classes. The results showed sigificat correlatio betwee ASM, CON ad DIS, ad values spread over wide rages, so a pricipal compoets aalysis (e.g. Rees, 199) was performed o the results i order to geerate ucorrelated texture measures. Before performig this aalysis, the data were trasformed to their logarithms to produce distributios which were more early ormal. The three pricipal compoets were foud to be (igorig additive cos ta ts) PC I = +.72 log (AS M) -.58 log (CON) log (DIS) PC 2 = log (ASM) log (CON) log (DIS) PC 3 = +.1 log (ASM) log (CON) log (DIS) The third pricipal compoet cotaied less tha.2% of the total variace ad was igored. Figure 3 shows the first two pricipal compoets, plotted as a cluster diagram. It ca be see from the figure that wet sow ca be distiguished from other classes by the value of PCI, ad that dry sow ca be distiguished from other classes by the value of PC 2 Other separatios are less obvious, but we ca quatify them as follows. We ca defie a simple idex of separability S for two probability distributios PI(X) ad P2(X) as S = 1- JpI(X)P2(X)dx J p'f(x)dx J pi(x)dx such that S = if the two distributios are idetical, ad S = 1 if they do ot overlap. Separability idices are calculated for each of the te possible pairs of image 252

4 Rees ad Li: Texture-based classificatio of cloud ad ice-cap features classes, usig first PC l ad the PC 2, ad the higher value of S is chose. A matrix of separability values ca the be defied: Melt Ablatio Wet Cloud Dry Melt Ablatio Wet.99 Ifwe assume that S must be greater tha about.8 for a effective discrimiatio, we ca see from this table that the GLCM techique provides poor discrimiatio betwee melt areas, ablatio areas ad cloud cover, but that other classificatios ad discrimiatios are uambiguous. Fractal techique Values of the fractal dimesio D ad the shift parameter were calculated for 432 trasects of the three images, agai chose to represet each of the five image classes. Figure 4 shows the resultig histograms for each class. It ca be see from the figure that wet ad dry sow surfaces have very similar, ad high, values of D. Ablatio areas show somewhat smaller values of D, ad melt areas ad cloud cover show very broad distributios. The shift parameter is lowest for cloud, itermediate for ablatio areas, ad highest for wet sow, dry sow ad melt areas. Separability statistics have also bee calculated for the 5 classes o the basis of the distributios of D ad the shift parameter, agai takig the higher calculated value of S for each pair of classes, with results as follows: Melt Ablatio Wet Cloud Dry Melt Ablatio Wet.95 The aalysis shows that the use of the fractal dimesio D aloe is a rather poor discrimiat of image class, although with the iclusio of shift data the co : co <D g> <D Cl le 6 I,\ 5 /1 \\ 4 /1 \\ 3 / i\ 1/, L 2 \, \ 1./... ~/.../'.r-,. --' ~./...- 7"... ~"""--I. """ Fractal dimesio Fig. 4. Histograms of the values of (a) fractal dimesio D ad (b) the shift parameter, from the fractioal Browia motio aa(ysis. The symbols are as i Figure 3. umber of ambiguous discrimiatios is reduced to four, amely dry sow from wet sow ad melt areas, ad melt areas from ablatio areas ad wet sow. If the results of the FBM ad GLCM separability aalyses are combied, we see that the umber of ambigous discrimiatios ca be reduced to oe, amely that betwee melt areas ad ablatio areas. DISCUSSION Our aalysis shows that the GLCM techique is, i geeral, able to make a more effective discrimiatio of image classes tha is the FBM techique, although the latter achieves a greater discrimiatio betwee cloud ad melt areas, ad betwee cloud ad ablatio areas. However, it is less profitable to thik of the techiques as rivals, but rather as complemetary methods which, whe combied, achieve a high level of discrimiatio i virtually all cases. It is useful to compare the computatioal efficiecies of the two texture algorithms. For a widow of size A X B pixels (A ~ B), the umber of operatios required to calculate the GLCM Sij is approximately AB as log as the modulus of the vector v is much less tha A or B. If the total umber of possible grey levels is, the umber of computatios required to calculate each of the three texture parameters defied i sectio 2 is 2. Thus the total umber of operatios required to geerate the set of three texture parameters is approximately AB Takig A = 24, B = 36 ad = 256 gives approximately 2 X 1 5 operatios. However, i practice, the rage of grey levels preset i Ladsa t images of sow, ice or cloud is very restricted, ad it is more realistic to take ~ 1. I this case, the umber of operatios required to geerate the GLCM parameters is closer to 1 3. For a trasect of legth A pixels, the umber of operatios required to geerate a semivariogram with m poits is approximately ma, ad the umber of operatios required to perform the iterative curve fittig ad the extractio of the texture parameters defied i sectio 2 is approximately 3m 2 The total umber of operatios required is about ma + 3m 2. Takig A = 3 ad m = 12 (typical values for the size of widow empoyed i this work) gives a total of about 1 3 operatios, so that the FBM ad GLCM techiques should i practice require similar amouts of computatio. This was cofirmed experimetally durig the course of this work. However, we stress that, i priciple, the calculatio of GLCM-based texture measures ca ivolve up to 1 times as much computatio as the FBM measures, ad that the complete classificatio of a image by this method could become prohibitive, requirig up to about 1 9 operatios. We emphasise the prelimiary ature of the work described i this paper. It is ope to criticism o the grouds that we have used data from several images ad two spectral bads, rather tha just oe of each, which raises the posibility that differetial calibratio ad su agle effects may affect our results. It must also be stressed that the use of texture aalysis i image classificatio should ot be relied upo util the physical mechaisms resposible for geeratig the image texture are clearly 253

5 Rees ad Li: Texture-based classificatio of cloud ad ice-cap features uderstood. At preset this ca be doe oly i a very broad ad qualitative way by recogizig that differet surface morphologies will give rise to differet image textures, but we are curretly attemptig to put this uderstadig o a quatitative basis. For example, the high value of the fractal dimesio D measured for wet ad dry sow areas ca be uderstood sice i these areas variatios i image brightess are likely to be caused predomiatly by topographic variatio (Rees ad Dowdeswell, 1988), ad this is small i the areas studied (Dowdeswell ad McItyre, 1987). Lower values of D for melt areas probably reflect real variatios i the surface structure at scales of less tha the image widow size of 2 km. If a uderstadig at this level ca be quatified ad exteded to iclude the GLCM parameters, it should allow questios such as the optimum spatial resolutio ad widow size, ad the optimum spectral bad (or combiatio of bads) to be determied. However, we believe that the geeral coclusio from this work will cotiue to stad, amely that the use of texture techiques (probably a combiatio of GLCM ad fractal techiques) has the potetial to provide uambiguous classificatios of images of sow, ice ad cloud, ad that this classificatio ca ultimately be automated. CONCLUSIONS We have applied two texture measures to three temporally separated Ladsat MSS images of the Nordaustladet ice cap. The results show that differet image classes have distict textures, ad cofirm that texture aalysis provides powerful additioal iformatio for the classificatio of such images. The results show that the use of the three GLCM texture parameters (a.gular secod momet, cotrast ad dissimilarity) gives i most cases greater discrimiatio of surface classes tha the use of the fractioal Browia motio techique. However, the two techiques appear to have differet stregths ad weakesses, ad their combiatio yields a high level of discrimiatio of all the image classes studied. ACKNOWLEDGEMENTS We gratefully ackowledge the techical assistace ofdrs B.J. Devereux ad J. A. Dowdeswell, ad of Messrs O. Dikshit, M. R. Gorma, T. Mayo ad G. Hamilto. The commets ofdrs R. Bidschadler ad R. A. Massom, ad of a aoymous referee, have greatly improved the presetatio of this work. REFERENCES Curra, P.] The semivariogram i remote sesig. Remote Sesig Eviro., 24, Dowdeswell, ]. A Remote sesig studies of Svalbard glaciers. (Ph.D. thesis, Uiversity of Cambridge.) Dowdeswell,J.A. ad N.F. McItyre The surface topography of large ice masses from Ladsat imagery. ]. Glaciol., 33, Feder,] Fractals. New York, Pleum Press. Haralick, R. M., K. Shamugam ad L., Distei Textural features for image classificatio. IEEE Tras. systems, maagemet & cyberetics, SM C3, Haralick, R. M Statistical ad structural approach ot texture. Proc. IEEE, 67, Key,] Cloud cover aalysis with Arctic Advaced Very High Resolutio Radiometer data 2. Classificatio with spectral ad textural measures. ]. Geophys. Res., 95, Li, 1. ad W. G. Rees. I press. A ew fractal texture classificatio of cloud ad ice cap surface features from Ladsat imagery. Proceedigs IGARSS '92. Mather, P. M Computer processig of remotely-sesed images. Chichester, etc., Joh Wiley. Orheim, o. ad B. K. Luchitta Sow ad ice studies by Thematic Mapper ad Multispectral Scaer Ladsat images. A. Glaciol., 9, Rees, W. G Physical priciples of remote sesig. Cambridge, Cambridge Uiversity Press. Rees, W. G Measuremet of the fractal dimesio of ice-sheet surfaces usig Ladsat data. It. J. Remote Sesig, 13, Rees, W. G. ad J. A. Dowdeswell Topographic effects o light scatterig from sow. Proc. IGARSS'BB Symp. ESA SP-2B Swithibak, C Ladsat images of Atarctica. u.s. Geo!. Surv. Pro! Pap. 1386B. Welch, R. M., M. S. Navar ad S. K. Segupta The effect of spatial resolutio upo texture based cloud field classificatios. J. Geophys. Res., 94, 14,767-14,781. Welch, R. M., K-S Kuo ad S. K. Segupta Cloud ad surface textural features i polar regios. IEEE Tras. Geosci. Remote Sesig, 28, The accuracy of rifereces i the text ad i this list is the resposibility of the authors, to whom queries should be addressed. 254