Segmentation and Classification of Hyperspectral Data using Watershed
The paper presents a new segmentation and classification scheme to analyze hyperspectral (HS) data. The Robust Color Morphological Gradient of the HS image is computed, and the watershed transformation is applied to the obtained gradient. After the pixel-wise Support Vector Machines classification, the majority voting within the watershed regions is performed. Experimental results are presented on a 103-airborne ROSIS image, of the University of Pavia, Italy. The integration of the spatial information from the watershed segmentation into the HS image classification improves the classification accuracies, when compared to the pixel-wise classification.