Abstract
The purpose of this study is to improve accuracy of land cover classification in urban area. This study used Quick bird and airborne hyper spectrum sensor AVIRIS. Land cover survey was attempted at residential area. There were houses, road, pond, park and vegetations. There were many kinds of vegetations, broad leave trees as Ash, Elm, Willow, Maple, Cotton tree, Needle leave trees as Pine, Tsuga, Spruce. About Quick Bird analysis, supervised classification (Maximum likelihood algorithm) was executed with some supervisors obtained from field study. 6categories were classified. The results show that with Quick Bird analysis, distribution of vegetation is comprehended well, but timber species are not comprehended well. About AVIRIS analysis, SAM (Spectral Angle Mapper classification) was executed with some supervisors obtained from field study. About broad leave trees, there were five species which spectrums were classified to categories. About grass, 3 categories were classified with conditions of land coverage condition. The accuracies of classification were 26% to 84%. It is confirmed that better results were obtained with AVIRIS.
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