Classification based marker selection for watershed transform of hyperspectral images

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.