IMPROVEMENT OF CLASSIFICATION ACCURACY WITH CONSIDERATION OF THE MIXTURE OF LAND COVERS

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1 IMPROVEMENT OF CLASSIFICATION ACCURACY WITH CONSIDERATION OF THE MIXTURE OF LAND COVERS Rysuke Shibasaki Public Wrks Research Institute Ministry f Cnstructin 1. Asahi, Tsukuba, Ibaraki, Hi Shimamura PASCO Crpratin 3-5, H iyama. i Shi ishi Crpratin yama, Cmmissin VJI/4 ABSTRACT Several tri s have been made t measure tree cver rati in areas thrugh classificatin land cvers satellite s data. In the trials a maximum likelihd methd (MLM) 1 y app 1 i ed. Hwever, the trials have nt always ven satis try results in terms measurement accuracy. Because an rdi Ii Iihd nt allw reslving percentage f tree cver n an IFOV, areas many f tree cvered areas are t t ccupy a large t be ignred measuring purpse. Authrs cnsideratin f the mixiture f 1 cvers and appl i it t t a measuring tree cver rati in areas a data. The th cnsi in f the mixture requires n ning cver classes. In terms f measurement accuracy, test cases with cnsideratin f the gave result 7% in RMSE) an rdinal MLM.

2 INTRODUCTION Vegetatin cver rati r tree cver rati is ften used as ne f the mst catr envirnments. Tree cver rati has been usually measured manually clr aeri Hwever, manual phtin and measurement f tree area require much labr and time, and has prevented frm measuring tree cver rati in many urban areas. 11 i te image data, such as Landsat data, thugh their grund reslutins are inferir t aerial includes multispectral in f land cvers in larger areas. Sme trials have been made t measure tree cver rati thrugh classificatin land cvers with satellite image data, in ch a maximum likelihd methd (MLM) has been usually emplyed. thse tri s nt given satisfactry results in terms f mea surement accuracy. In urban area, especially in Japan, lands are densely utilized and many 1 tree cvered areas are scatteread in 'nn-tree' area. Each tree cvered area is t small t ccupy a whle IFOV f satellite image and t be classified as 'tree', t arge t in measuring tree cver rati fr assessing rnment. nt allw res ng the tree cver wi thin IFOV f sate 11 i te images, and fails t pick up p Is in the tree cver is relat ly lw. This cnstraint wuld be thught t cause serius errrs in measuring tree cver rati in urban areas. an wi cnsi in xture f land cvers and applied it t measuring tree cver rati in urban areas. Three test cases were assessing the perfrmance f the MLM with the cnsi in f the mixture in cmparisn an rdinal MLM. PREVIOUS STUD There exist tw main grups cncerning methds f reslving the mixture f cvers. One grup cnsists f the methds based n linear regressin Is which relate the percentage f a bject-land cver t the cmbinatin f reflectance values in bands and/r sme kinds f indices, such as tatin I ned frm reflectance ues. Hwever, ilding linear

3 regressin mdels requires pixels in which the prprtin f an bject-land cver is exactly determined. Acquisitin f the prprtin data in each pixel is encumbered in practical applicatins because it is nt easy t determine accurately the exact lcatins f the mixed pixels. The ther grup is based n a general weighted average equatin. Under the assumptin that mean reflectance vectr f a mixed pixel nearly equals the weighted average f cmpnent class mean vectrs, the prprtin f each cmpnent is given as a slutin f a weighted average equatin. Hwever, it is necessary that tw imprtant cnstraints shuld be bserved [3J : (1) n (pure) cmpnent class mean vectr in a mixture must be similar t a weighted average f the ther cmpnent class mean vectrs. (2) in case the number f spectral bands available is less than the number f cmpnents, estimatin accuracy f the prprtin is pr. An apprximate MLM [1J wuld be thught f an extensin f an rdinal MLM. The methd yields the prprtin f a cmpnent thrugh calculating the differences f Mahalanbis distances between a mixed pixel and each cmpnent class f the mixture. The methd prved prmising in five test cases fr gelgical applicatins. A CLASSIFICATION METHOD WITH CONSIDERATION OF THE MIXTURE Figure 1 shws a classificatin prcedure with cnsideratin f the mixture f land cvers. The prcedure is based n MLM. MLM is widely used in image classificatin and many image prcessing systems prvide prgrams f MLM. At the first stage f the prcedure, sme f pixels f pure cmpnents are extracted fr training sets. At the same time, mean vecters and cvariance matrices f mixed classes are estimated thrugh weighted average f mean vectrs and cvariance matrices f pure cmpnent classes. The weight values are prprtinal t the percentages f pure cmpnents. The stepwise prprtin f mixture are given fr mi classes. Step width is determined with the cnsideratin f degree f spectral separatin between pure classes. Bth extractin f nly pure pixels and estimatin f mean vectrs and cvariance matrices f mixed classes are nt unencumbered because the exact lcatins f nly pure cmpnent pixels must be determined and the standard f pixel extractin fr pure pixels is clear. The first classificatin th MLM is

4 Training Set Acquisitin f Pure Cmpnent Classes (Tree, Flat Huse etc) Training Sets I Estimatin f Mean Vectrs and Cvariance Matrices f Mixed Classes thrugh Weighted Average f Pure Cmpnent Class Mean Vectrs and Cvariance Matrices eg.) Tree + Huse 20% 80~~ H-' 1st Classificatin with M L M fr Pure Classes Unclassified Pixels 2nd ClassIficatin wi th M L M fr Mixed Classes Pixels Classified in Mixed Class Pixels Classified int Pure Class 11, Ca.lculatin f Tree Cver Area (Tree Cver Rati) F 1 MLM Based Classificatin Prcedure th Cnsideratin f the Mixture f Land Cvers (MLM: Maximum Likelihd Methd)

5 carri ut nly pure classes. In first classificatin sts a threshld Mahal is distance lectance vectr f an bjec 1 nearest pure cmpnent class mean vectr. A pixel whse minimum anbis distance is larger the threshld remains unclassifi In test cases, ass the ld t be 2a. At p Is unclassifi at first stage are classifi int mi classes. amunt tree cver pixel yiel tree cver area. ass in class mean vectrs we average pure cmpnent class mean exists serius discriminatin is icult in case mean vectr a class happen t cme clse t mean vectr f any ther class. assume that there much mre pixels pure classes than pixels f classes, and priri t pure classes in classificatin. classificatin algri lects s assumptin in a simplified st COMPARISON ON ACCURACY Fr the cmparisn f classificatin accuracy, three test cases were Tw test areas, Hnmku (1), are near Ykhama Prt, abut 30km stant frm center f and (2) include large residenti area tree cvered areas The ther test area, Khku Twn (NT) is lcated abut 20km stant area cnsists tree areas, res i areas test areas is x in size, is di is 30mX in size. Actual tree cver rati interpretatin clr aeri 3 actual tree cver rati cells in c tree cver rati btai classificatin f a 1 y, cnsi in mixture are in 4, cells were classifi int pure classes in Ie L cmpar i sn, tree cver ratis an are in Pht 7, 8 9, A classificatin wi t first s classificatin in F 1 3a Id

6 Actual Tree Cver MLM with Cnsideratin f Mixture Ordinal MLM (threshld ;30') H n III k u 1 Pht 1 Pht 4 Pht 7 H n m k u 2 Pht 2 Pht 5 Pht (3 K h k u N T Pht 3 Pht 6 Pht 9 100~~ Pht 1~9 Cmparisn f Measurement Accuracy f Tree Cver Rati 6 99~,,; 0%

7 Table. 1 Pure and Mixed Land Cver Classes fr Test Cases Pure Classes Mixed Classes Tree 1 80% Flat Huse 20% Tree 2 60% - 40% Flat Huse 40% - 60% - 20% - High Rise Apartment Tree 80% HRA 20~~ (HRA) - 60~'6-40% -- 40% - 60% - 20% - 80% Grass 1 Grass 80% Bare Land 20% Bare Land 1 60% - 40% % - 60% 3-20% - 80% Table. 2 Cmparisn f Measurement Accuracy f Tree Cver Rati Case Tree Cver rati f Test area (%) Hnmku 1 I Hnmku 2 J Khku Nt RMSE (~~) Data Actual Aerial Phts (- ) ( ) (- ) - (Aug. 1984) Ordinal MLM (.,-5. 5) (-3.7) (-2.3) 4. 1 Landsat TM MLM with Cnsideratin f the Mixture (-2.1) (0.7) (1.9) 1.7 (July. 1987) (Discrepancies (MLMs - Actual) are within parentheses) VII

8 minimum Mahalanbis distance. The results with an rdinal MLM ( Pht 7"--9 ) shw that many f pixels in which tree cver ratis are relatively lw are classified int nn-tree classes. Table 2 shws tree cver rati f each test area. In every case, tree cver rati btained with the MLM with cnsideratin f the mixture cmes clser t actual rati than that with an rdinal MLM. Measurement accuracy with the, MLM with cnsideratin f the mixture is 1.7% in RMSE, while accuracy with an rdinal MLM is 4.1% in RMSE. In these test cases, an rdinal MLM has tendency f underestimatin because a MLM failed t pick up pixels in which tree cver rati is relatively lw. CONCLUSIONS (1) A classificatin methd with cnsideratin f the mixture f land cvers was develped. The results f three test cases shwed the measurement accuracy f tree cver rati with the classificatin methd with cnsideratin f the mixture is 1.7% in RMSE, which is better than that with an rdinal MLM in every case. (2) The classificatin methd with cnsideratin f the mixture is based n MLM. and requires n training sets fr mixed land cver classes. These mean that the classificatin methd with cnsideratin f the mixture des nt require much mre time and labr than an rdinal MLM des in practical appl icatins. REFERENCES [lj March, S.E., Switzer, P., Kwalik, R. J.P.; Reslving the Percentage f Cmpnent Terrains within Single Reslutin Elements, Phtgrammetric Engineering and Remte Sensing, Vl.46, N.8, 1980, pp.l [2J Wrk, E.A.. Arbr, A., Gilmer. D. S.: Utilizatin f Satellite Data fr Inventrying Prairie Pnds and Lakes, Phtgrammetric Engineering and Remte Sens ing, vl. 42. N.5, 1976, pp [3J Matsu, Y.; Theretical Cnsideratins n the Estimatin Methd f Land Cver Mixing Rati Us ing MSS Data. Trans. J. S. 1. D. R. E. Vl. 116, Apr. 1985, pp (in Japanese). VII-451

B Tech Project First Stage Report on

B Tech Project First Stage Report on B Tech Prject First Stage Reprt n GPU Based Image Prcessing Submitted by Sumit Shekhar (05007028) Under the guidance f Prf Subhasis Chaudhari 1. Intrductin 1.1 Graphic Prcessr Units A graphic prcessr unit