Classification based marker selection for watershed transform of hyperspectral images

Abstract

A new method for segmentation and classification of hyper-spectral images is proposed. The method is based on a pixel-wise classification followed by selection of the most reliable classified pixels as markers for watershed segmentation. Furthermore, each marker defined from classification results is associated with a class label. By assigning the class label of each marker to all the pixels within the region grown from this marker, a spectral-spatial classification map is obtained. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana's Indian Pine site. The developed segmentation and classification scheme significantly decreases oversegmentation, improves classification accuracies and provides classification maps with more homogeneous regions, when compared to pixel-wise classification or previously proposed spectral-spatial classification techniques.