Unsupervised classification of hyperspectral images by using linear unmixing algorithm

In this paper, we present an unsupervised classification algorithm for hyperspectral images. For reducing the dimension of hyperspectral data, we use a linear unmixing algorithm to extract the endmembers and their abundance maps. Compared to the components obtained by traditional PCA-based method, the abundance maps have physical meanings (such as the abundance of vegetation). For determining the number of endmembers contained in an image, we propose an eigenvalue based approach. The validation of this approach on synthetic data shows that this approach provides a robust estimation of the actual number of endmembers. Using the estimated abundance maps of the endmembers, we perform a preliminary segmentation and use the mean values of the segmented regions as feature for the classification. We then perform K-means classifications on the segmented abundance maps with the number of clusters determined by the Krzanowski and Lai´s method.