Hyperspectral Data Clustering Using Hellinger Divergence

Abstract

Clustering is an important task in hyperspectral image processing. Despite the existence of a large number of clustering algorithms, little attention has been paid to the use of non-Euclidean dissimilarity measures in the clustering of hyperspectral data. This paper proposes a clustering technique based on the Hellinger divergence as a dissimilarity measure. The proposed technique uses Lloyd’s ideas of the k-means algorithm and gradient descent-based procedure to update clusters centroids. The proposed technique is compared with an alternative fast k-medoid algorithm implemented using the same metric from the viewpoint of clustering error and runtime. Experiments carried out using an open hyperspectral scene have shown the advantages of the proposed technique.