Land-use/land-cover classification with multispectral and hyperspectral EO-1 data

We compared the capability of the Earth Observing-1 (EO-1) Hyperion hyperspectral (HS) data with that of the EO-1 Advanced Land Imager (ALI) multispectral (MS) data for discriminating different land-use and land-cover classes in Fremont, California. We designed a classification scheme of two levels with level I including general classes and level II including more specific classes. Classification shows that the HS data does not produce better results than the MS data when we directly applied a Mahalanobis distance (MD) classifier. We tested a number of feature reduction and extraction algorithms for the HS image. These algorithms include principal component analysis (PCA), segmented PCA (SEGPCA), linear discriminant analysis (LDA), segmented LDA (SEGLDA), penalized discriminant analysis (PDA) and segmented PDA (SEGPDA). Feature reductions were all followed by an MD classifier for image classification. With SEGPDA, SEGLDA, PDA, and LDA, similar accuracies were achieved while a segmentation-based approach we proposed (SEGPDA or SEGLDA) greatly improved computation efficiency. They all outperformed SEGPCA and PCA by 4 to 5 percent (level II) and 1 to 3 percent (level I) in classification accuracy. For level II classification, overall accuracies obtained by using the features extracted from the HS image were 2 to 3 percent greater than those obtained with the MS image. For various vegetation class and impervious land use categories, the HS data consistently produced better results than the MS data. For level I classification, the HS image generated a thematic map that is � 0.01 greater in kappa coefficient comparing to the MS image. When we collapsed the level II classification map to a level I map, 5 percent (HS) to 7 percent (MS) improvements were achieved.