Hyperspectral land cover classification of EO-1 Hyperion data by principal component analysis and pixel unmixing

In this paper, we attempt to perform land cover classification using hyperspectral data acquired by the EO-1 Hyperion instrument over two test sites in the tropical region: one in Singapore and the other one in coastal Jambi on the Sumatra island of Indonesia. Atmospheric correction on the hyperspectral imagery was first performed using a commercial package. Principal component decomposition was then performed and an unsupervised ISODATA classification was carried out on the dominant components to produce a land cover classification map for each test site. Classification using the pixel unmixing method as implemented in the ENVI package was also performed. The results of classification were compared with existing land cover maps.