DIFFERENT APPROACHES IN FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGE CLASSIFICATION

This paper describes different approaches in feature extraction for a hyperspectral image classification. For the actual feature extraction, principal components transformation, band correlation method, average intensity of the visible/infrared ranges and spectral knowledge are used. The output of each of the feature extraction method is used for a classification process. The results are analyzed and compared.