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In this paper we present a new dynamical systems algorithm for clustering inhyperspectral images. The main idea of the algorithm is that data points are\`pushed\' in the direction of increasing density and groups of pixels that endup in the same dense regions belong to the same class. This is essentially anumerical solution of the differential equation defined by the gradient of thedensity of data points on the data manifold. The number of classes is automatedand the resulting clustering can be extremely accurate. In addition toproviding a accurate clustering, this algorithm presents a new tool forunderstanding hyperspectral data in high dimensions. We evaluate the algorithmon the Urban (Available at www.tec.ary.mil/Hypercube/) scene comparingperformance against the k-means algorithm using pre-identified classes ofmaterials as ground truth.

A Dynamical Systems Algorithm for Clustering in Hyperspectral Imagery