earticle

영어

This paper presents a new technique for marker selection called marker selection using skeletonization. Markers are the most reliable pixels that represent a particular class. Marker selection using skeletonization is further analysed to do classification of hyperspectral image with very low training samples, as low as one pixel per class. Both spatial and spectral information are used to improve the final classification accuracy. An Extended Morphological Profile with duality is used to extract spatial information. Furthermore, it is shown that by using the spatial and spectral information with non- parametric supervised feature extraction methods, better classification accuracy can be achieved even when very low training samples are available. The classification maps will be shown and discussed for very low training sample analysis using marker selection by skeletonization technique.