Abstract

A simulated annealing method of partitioning hyperspectral imagery, initialized by a supervised classification method, is investigated to provide spatially smooth class labeling for terrain mapping applications. The method is used to obtain an estimate of the mode a Gibbs distribution defined over a symmetric spatial neighborhood system that is based on an energy function characterizing spectral disparities in Euclidean distance and spectral angle. Experiments are conducted on a 210-band HYDICE scene that contains a diverse range of terrain features and that is supported with ground truth. Both visual and quantitative results demonstrate a clear benefit of this method as compared to spectral-only supervised classification or unsupervised annealing that has been initialized randomly.

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