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

摘要

The capability of the Earth Observing-1 (EO-1) Hyperion hyperspectral (HS) data were compared 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, USA. The diverse landscapes in the study area range from hilly regions in the north, the urban area in the middle, and the salt evaporators in the southern end of San Francisco Bay. A classification scheme of two levels with level I including general classes and level II including more specific classes was designed. Classification showed that the HS data does not produce better results than the MS data when a Mahalanobis distance (MD) classifier was directly applied. A number of feature reduction and extraction algorithms for the HS image were tested. 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 proposed approach (SEGPDA or SEGLDA) greatly improved computation efficiency. They all outperformed SEGPCA and PCA by 4 to 5% (level II) and 1 to 3% (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 the level II classification map was collapsed to a level I map, 5% (HS) to 7% (MS) improvements were achieved.