THE AUTOMATIC CLASSIFICATION OF B&W AERIAL PHOTOS

Transcription

1 THE AUTOMATIC CLASSIFICATION OF B&W AERIAL PHOTOS L. Hlounov Remote Sensing Lbortory, Fculty of Civil Engineering, Czech Technicl University Prgue Thkurov 7, Prgue 6, Czech Republic, KEY WORDS: Lnd Cover, Photogrphy, Texture, Fuzzy Logic, Segmenttion, Aeril, Multiresolution ABSTRACT: The pper shows possibility to clssify B&W eril orthophotogrphs nd other monochromtic remote sensing dt using imge enhncement phse nd object-oriented nlysis phse for the utomtic clssifiction. The first phse enlrges the spectrl signture spce by chnnels clculted by imge filtering (medin filter nd Guss filter) nd by texture mesures. The combintion of vrious filter sizes (texture mesures) nd kernel sizes (filters) enlrges the signture spce llowing the following imge segmenttion nd clssifiction in two or three scle levels. The t lest two level clssifiction simplifies themticlly complex eril orthophotogrphs by dividing the photo into themticlly more homogenous res in the higher level. The lower level brings the finl sought clsses, which cn be slightly corrected in the third (lowest) level. The ecognition softwre ws used for the imge segmenttion nd for the utomtic clssifiction. It is the first method of utomtic clssifiction of lnd cover of monochromtic remote sensing dt bringing ccurcy better thn 8 per cent. 1. INTRODUCTION The pper uthor ws responsible person for nlyzing one prt of project of the Czech Ministry of Agriculture. The im of the project ws to find solutions for utomtic informtion extrction from B&W eril orthophotogrphs. The scle of these eril photogrphs ws 1: 23. Ech othophoto ws result of mosicking. The clssicl wy of utomtic clssifiction is bsed on close spectrl signtures or other signtures of pixels representing the sme clsses. This ssumption is not vlid in cse of B&W photogrphs where different res re formed by pixels with the sme vlues nd on the other side one clss is formed by wide rnge digitl vlues. The signture spce for individul clsses overlpped nd could not hve been used for their distinguishing in this stte. No references were found to show solution for the similr monochromtic dt type. Known imge enhncement methods were pplied to be obtined more seprble clss signture spce for individul clsses. The utomtic pixel-by-pixel clssifiction ws excluded from the nlysis nd replced by the object-oriented clssifiction performed for segmented imge dt. Segmenttion hs been used by severl specilists who pplied vrious interprettion keys (Borisov et l., 1987, Jgtp et l., 1994, Nesset, 1996, Žihlvník, Plg, 1995). The segmenttion used in this project ws the Frctl Net Evolution Approch (FNEA) commercilly introduced by Btz nd Schäpe (1999) incorported in commercil softwre ecognition. The proposed method cn be pplied for lrge number of with reltively high level of utomtion. Tht ws the project gol. The result of the project should be pplied for the whole country nd therefore their imge processing operbility ws necessry. 2. METHODOLOGY There were two min tsks in the imge processing. To enlrge signture spce of individul clsses to be seprble ws the first tsk. To perform the utomtic clssifiction formed the second tsk. 2.1 Signture spce enlrgement Signture spce enlrgement ws done by using two wys of new chnnel clcultion. One wy ws the imge filtering by low-pss filters where Medin filter nd Guss filter were pplied. They supressed locl imge unhomogeneities. They showed high correltion with originl imge dt. Tht ws the reson why they did not ssure sufficient clss seprbility. Chnnels clculted from two kernel sizes nd repetble filtering were used within the project. Chnnels filtered by Guss filter were clculted for stndrd devition equl to 2, 3, nd 4. Another tool ws necessry for the successful solution. Hrlick functions were the tool s functions chrcterizing textures. There re more Hrlick functions used in imge processing. Different numbers of them were used for different level of clssifiction detil. Hrlick functions were tested for severl window sizes. Window sizes depended on individul clss member sizes. Smller window sizes were useful for smll resulting clss members sizes. The tests showed tht there were no previling trends in certin direction nd tht ws the reson why ll directions where reltions between pixels re determined, were tken into ccount. All directions ment tht differences between pixel nd reference pixel were clculted for directions, 45, 9, nd 135. These differences were used for GLCM (Grey Level Co-occurrence Mtrix) nd GLDV (Grey Level Difference Vector) clcultions from Hrlick functions. Men, stndrd devition nd dissimilrity functions were chosen for the orthophoto clssifiction defined in following expressions. Their window size were 5x5 pixels, 11x11 pixels, nd 21 x 21 pixels.

2 Men _ i = n 1 n 1 i= j = St _ devition = Dissimilr ity = P i i, j. n [ 1 n 1 2 Pi, j ( i Men _ i) ] i= j= n 1 n 1 i= j= P i, j. i j where P i,j is the normlized grey level co-occurrence mtrix (GLCM) with n x n size, where originl n = 256 for 8bit dt is pre-scled to n=16 such tht n 1 P ( i, j) = 1. i, j= 2.2 Object-oriented nlysis Problem with wide rnge of digitl vlues representing one themtic clss nd overlpping vlues for individul clsses were prtly solved by the previous step new chnnel clcultions. This step hs not solved ll problems with overlpping vlues of individul clsses. Tht ws imge segmenttion wht helped to divide imge dt into lrge regions representing importnt prts of lnd use. This segmenttion llowed seprting of urbn prts from griculturl prts nd from forest res. The segmenttion performed by object-oriented nlysis defined in ecognition softwre simplified themticlly complicted imge dt content. The object-oriented nlysis comprises two prts. The first one prepres imge dt by creting segments from them nd the second one llows their clssifiction. The segmenttion is bsed on heterogeneity evlution. The heterogeneity is chrcterized either by spectrl heterogeneity, or sptil heterogeneity nd their mutul combintion. Higher influence of spectrl heterogeneity is ccomplished by lower sptil heterogeneity while their sum is equl to 1. The sptil heterogeneity compres either the compctness tking into ccount segment length nd its number of pixels, or the smoothness expressing reltion between segment length nd the shortest segment length defined by rectngle circumscribing the segment. 2.3 Clss definition Two-level clss definition ws the result of the presented methodology. The higher-level segmenttion served to clssifiction into bsic regions: old forest, young forest griculturl re, urbn re. The lower-level clssifiction comprised higher number of clsses. Ech of them belonged into the only higher-level clss. The following list shows clsses for the lower-level (more 1 2 detiled clssifiction). Clss nmes re creted from three prts. The first prt prt (F = forest, NF = nonforest, A= griculturl re nd U urbn re) defines the rel sitution of segments derived from the lower-level clssifiction. The second prt determines belonging to higher-level clssifiction where OF mens old forest, YF represents young forest, AA is n bbrevition for griculturl re nd UA urbn res. The third prt of clss nmes indictes the lower-level clss. The complete lower-level clss nmes re: F_OF_coniferous forest, F_OF_deciduous forest, F_OF forest older thn 7 yers, NF_OF forest up to 7 yers, F_YF coniferous forest, F_YF deciduous forest, F_YF forest older thn 7 yers, NF_YF forest up to 7 yers U_YF rod, F_AA tree, A_AA field, U_AA house U_AA rod, F_UA tree, NF_UA green re, U_UA light house, U_UA drk house, U_UA house, U_UA rod. The first prt of nmes shows possible regrouping of certin clsses, which were originlly clssified into themticlly wrong higher-level clsses. This process of regrouping brings new improvements into the nlysis. The forest clss is formed by three clsses from old forest, three clsses from young forest nd one clss from griculturl res nd one clss from urbn res. Clss definitions nd their list were dpted to rel sitution in the imge dt nd cn chnge from to region to region. 2.4 Segmenttion The segmenttion clled multiresolution segmenttion llows segmenttion in more levels. The higher-level segmenttion for imge dt division into themticlly simple regions ws clculted for high scle vlue (25) in the first imgeprocessing phse. The originl orthophoto nd the chnnel clculted by medin filter with 5x5 kernel size formed the input imge dt for the segmenttion processes in the higherlevel segmenttion. The lower-level segmenttion being the second imgeprocessing phse ws repetition of the first one for lower scle vlue (35 5 ccording to imge dt). The segmenttion process used the sme chnnels. The influence of spectrl heterogeneity vried from higher vlue for the higher segmenttion level to lower vlue for more detiled clssifiction where sptil heterogeneity plyed more importnt role. 2.5 Clssifiction After the segmenttion, segments cn be clssified into clsses. The segmented imge dt clssifiction ws done by the

3 nerest neighbor clssifier. The clssifiction signture spce used men segment vlues of orthophotogrph, chnnels clculted from medin filter nd Guss filter with three different stndrd devition vlues (equl to 2, 3, nd 4). The lst prt of signture spce ws creted by chnnels clculted s texture mesures. Texture mesures men, dissimilrity nd stndrd devition using three Hrlick functions were clculted for three window sizes 5x5 pixels, 11x11 pixels nd 21x21 pixels. Clssifictions in both levels used the sme signture spce. 3. RESULTS The higher-level clssifiction hd to be corrected in severl cses (severl segments). It is reltively quick prt of processing being performed during visul control nd being done mnully. Fig. 1 shows the originl imge dt nd Fig. 2 shows result of the higher-level segmenttion. The second-level clssifiction result is on Fig. 3. The ccurcy of clssifiction result ws controlled in rndom smple res. The ccurcy ws clculted for producer s ccurcy PA(clss_i) defined by ( _ i) PA clss = N k = 1 nd for user s ccurcy UA(clss_i) ( i) UA clss k = 1 ii ki ii _ = N. ik The producer s ccurcy estimtes the probbility tht pixel, which is of clss_i of the reference clssifiction, is correctly clssified. The totl number of pixels of clss_i in the reference clssifiction is obtined s the sum of column i. The user s ccurcy estimtes the probbility tht pixel clssified s clss_i is ctully of clss_i. It compres the correctly clssified number of pixels of clss_i with the totl number of pixels clssified s clss_i. The totl number of pixels clssified s clss_i is in the row i. Clssified Reference clsses clsses house tree rod field forest up deciduous coniferous of pixels to 7y house tree rod field forest up to 7y deciduous coniferous of pixels Accurcies for individul clsses Producer s PA User s UA Tble 1. Results of clssifiction ccurcy. The best results re in yellow nd the worst in gry The overll ccurcy ws.9 with kpp coefficient equl to.87.

4 Figure 1. The imge dt (scnned B&W eril orthophoto) young forest griculture re urbn re old forest Figure 2. Higher-level clssifiction

5 Forest YF older thn 7 yers AA tree OF coniferous up to 45 yers OF coniferous older thn 45 y OF deciduous UA tree Non-forest UA green res AA fields YF forest up to 7 yers OF forest up to 7 yers Urbn re UA rod UA drk house UA light house UA house AA house AA rod YF rod Figure 3. Lower-level clssifiction Results shows tht certin prts of rods were clssified into clss houses. Pixels from clss houses were clssified into trees, rods, nd fields. Clssified clss clled tree comprised pixels from houses, rods, nd fields, clss rods ws clss hving lso pixels from houses. Forest clsses showed very good results for the coniferous forest. The forest younger thn 7 yers old ws clssified prtly into the deciduous forest. The best results were obtined for fields. Results relibility is higher between urbn res nd forests or urbn res nd fields. Smller relibility ws found between urbn clsses houses nd rods, etc. 4. CONCLUSION The B&W photogrph clssifiction cn be performed with reltively high ccurcy. There re three necessry conditions offering such good results. New chnnels hve to be clculted from originl dt (photogrph) chnnels clculted by filtering where medin nd Guss filters re used, nd chnnels using Hrlick functions. Creful testing of kernel sizes for filtering nd creful choice of filter window sizes for Hrlick functions compred to finl clssified object sizes cn sufficiently improve clssifiction ccurcy. The presented project used kernel sizes smller thn the smllest clssified objects. Filter window sizes were smller, equl nd little bit lrger thn these objects. The higher number of Hrlick functions improves clssifiction ccurcy. Object-oriented nlysis using imge segmenttion followed by segment clssifiction is the second necessry condition for good results of clssifictions. Two level segmenttion in the reverse order of segmenttion (from higher to lower) is the third condition. The higher-level segmenttion ensures frgmenttion of imge dt with overlpping pixel vlues for different clsses into themticlly closer nd smller imge prts whose further segmenttion nd clssifiction offers good results. The lower level clssifiction is performed individully for lredy frgmented prts of imge dt. The confusion in resulting clssifiction cn be found in urbn res. The overll ccurcy ws bout 9 per cent. However, the ccurcy for individul clsses vried from 5 per cent to 1 per cent. Low ccurcy vlues were in cse of two clsses trees whose pixels were clssified into deciduous nd coniferous forests. The clss tree did not distinguish tree types. It ws clss describing individul trees or tree groups out of forest res. Rther wrong distinguishing cn be found between very young forest nd deciduous forest. It is difficult to distinguish these two clsses even during visul interprettion especilly for forest ge younger thn 4 yers. REFERENCES Btz, M. nd Schäpe, A., Object oriented nd multiscle imge nlysis in semntic networks. Proc. Of the 2 nd Interntionl symposium on opertionliztion of remote sensing, August 16-2,, Enschede ITC. Borisov, A.N., Kshin, V.B., Khlebopros, R.G., Method for indiction of horizontl structure of tree stnds. Dokldy Biologicl Sciences, : (1 6), Hlounová, L., 23. Texturl clssifiction of B&W eril photos for the forest clssifiction. Proc. of the 23 rd symp. of EARSeL, Gent, Belgium, June 2-5, 23:

6 Hlounová, L., 24. Clssifiction of B&W eril photos nd of rdr dt. Hbilittion thesis. Czech Technicl University Prgue (in Czech) Hlounová, L. 23. Clssifiction of B&W eril photos. The 4 th Conference Actul Problems of Photogrmmetry nd RS, Prgue, September ( in Czech) Hrlick, R.M., Shnmugn K. nd Dinstein, I., Texturl Fetures for Imge Clssifiction, IEEE Tr. on Systems, Mn, nd Cybernetics, Vol. SMC-3, No. 6, pp , November Hrlick, R.M., Sttisticl nd Structurl Approches to Texture, Proceedings of the IEEE, Vol. 67, No. 5, pp , My Jgtp, T.G., Untwle, A.G., Inmdr, S.N., Study of mngrove environment of Mhrshtr cost using remote sensing dt. Indin Journl of Mrine Sciences. 23(2): Nesset, E., Determintion of number of stems in coniferous forest stnds by mens of eril photointerprettion. Scndinvin Journl of Forest Reserch, 11(1): 76-84, Žihlvník, S., Plg, J., Interprettion key-topogrphic elements nd tree species on colour syntheses, Lesnictví- Forestry, 41(1): ACKNOWLEDGEMENT The processed dt were used by the courtesy of the Ministry of Agriculture nd the reserch prt ws processed within the GA R project 25/3/218 reserch nd Vyzkumny zmer 217. The project presenttion ws finncilly supported by the Czech Technicl University grnt