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SEMI-SUPERVISED CLASSIFICATION OF LAND COVER BASED ON SPECTRAL REFLECTANCE DATA EXTRACTED FROM LISS IV IMAGE

A methodology is proposed for extracting information on land cover based on hyperspectral reflectance data derived from satellite image, without supervising with ground truth. The reflectance percentage, being a characteristic feature of the ground object acts as an indirect guidance to the classification and hence the method is named semi-supervised classification. It is tried with IRS LISS IV image of a specific part of southern West Bengal, India. At least three categories, viz. vegetation, waterbody and open land are distinctly identified with the present technique. It is hoped that a method like this is more useful in the analysis of hyperspectral imagery, which is an area of upthrust in future.