Unsupervised nonparametric classification of polarimetric SAR data using the K-nearest neighbor graph

Polarimetric SAR classifications are often based on assumptions about the shape of clusters in the data space. Such a scheme will fail for nonlinear structures in the feature space, unless the classification algorithm has the capacity to describe cluster shapes in sufficient generality. Existing polarimetric SAR classification methods are faced by this exact problem: typically they initialize clusters in the Cloude-Pottier parameter space [1], further optimizing them in the coherency matrix space [2, 3]. Methods using K-means [2] or agglomeration [3] require clusters that are spherical, or compact and well separated, respectively. In the Cloude-Pottier space, these requirements are not met, so initialization in the Cloude-Pottier space cannot be consistent with optimization by K-means or agglomeration. This paper sets out to address this problem, by implementing a new data-driven clustering approach, for arbitrarily shaped clusters. It is applied to quad-polarisation data, demonstrating the new methodology's potential for forest land-cover type discrimination.