CLASSIFICATION OF HYPERSPECTRAL IMAGES USING AUTOMATIC MARKER SELECTION AND MINIMUM SPANNING FOREST

A new method for segmentation and classification of hyperspectral images is proposed. The method is based on the construction of a Minimum Spanning Forest (MSF) from region markers. Markers are defined automatically from classification results. For this purpose, pixel-wise classification is performed and the most reliable classified pixels are chosen as markers. Furthermore, each marker defined from classification results is associated with a class label. Each tree in the MSF grown from a marker forms a region in the segmentation map. By assigning a class of each marker to all the pixels within the region grown from this marker, classification map is obtained. Furthermore, the classification map is refined, using results of a pixel-wise classification and a majority voting within the spatially connected regions. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana's Indian Pine site. The use of different dissimilarity measures for construction of the MSF is investigated. The proposed scheme improves classification accuracies, when compared to previously proposed classification techniques, and provides accurate segmentation and classification maps.