Improved Nearest Neighbor Density-Based Clustering Techniques with Application to Hyperspectral Images

We consider the problem of density-based unsupervised classification in hyperspectral data. Our focus is especially on methods based on K nearest neighbors (KNN) graph. In this paper, we propose some improvements of recently published methods in this vein, namely GWENN (Graph WatershEd using Nearest Neighbors) as well as a KNN version of Density Peaks Clustering. These improvements address (i) the structure of the KNN graph, which can be modified efficiently to emphasize the dependencies between objects, especially in high dimensional data sets; (ii) the choice of the pointwise density model; and (iii) the ability of these methods to handle variable NN graphs. The improved methods are compared in the context of pixel partitioning in hyperspectral images and are shown to give encouraging results, outperforming state-of-the-art methods like DBSCAN and FCM.