Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches

Unsupervised segmentation of hyperspectral satellite images is a challenging task due to the nature of such images. In this paper, we address this task using the following three-step procedure. First, we reduce the dimensionality of the hyperspectral images. Then, we apply one of classical segmentation algorithms (segmentation via clustering, region growing, or watershed transform). Finally, to overcome the problem of over-segmentation, we use a region merging procedure based on priority queues. To find the parameters of the algorithms and to compare the segmentation approaches, we use known measures of the segmentation quality (global consistency error and rand index) and well-known hyperspectral images.