Spatially smooth partitioning of hyperspectral imagery using spectral/spatial measures of disparity

A Bayesian approach to partitioning hyperspectral imagery into homogeneous regions is investigated, where spatial consistency is imposed on the spectral content of sites in each partition. An energy function is investigated that models disparities in an image that are defined with respect to a local neighborhood system. this energy function uses one or certain combinations of the spectral angled Euclidean distance, and/or Kolmogorov-Smirnov (mean-adjusted) measures. Maximum a posteriori estimates are computed using aim algorithm that is implemented as a multigrid process to improve global labeling and reduce computational intensity. Both constrained and unconstrained multigrid approaches are considered. A locally extended neighborhood structure is introduced with the, intention of encouraging more accurate global labeling. The present effort is focused on terrain mapping applications using hyperspectral imagery containing narrow bands throughout the 400-2500-nm spectral region. The trials of our experiment are conducted on a scene from HYDICE 210-band imagery collected over an area that contains a diverse range of terrain features and that is supported with ground truth. Quantitative measures of local consistency (smoothness) and global labeling, along with class maps, demonstrate the benefits of applying this method for unsupervised and supervised classification, where the best results are achieved with an energy function consisting of the combined spectral angle and Euclidean distance measures.