Hyperspectral image classification using discontinuity adaptive class-relative nonlocal means and energy fusion strategy

This paper presents an effective classification approach for hyperspectral image, based upon a novel discontinuity adaptive class-relative nonlocal means (DACNLM) algorithm and embedding it in the global energy function by energy fusion strategy. Inspired from recent works related to nonlocal means, we extend this framework to label space, assuming that nonlocal similar patches have similar label structures. Thus, similar local structures and nonlocal averaging process are combined by the proposed DACNLM algorithm. The Shannon entropy is adopted to define the distribution of energy. The energy function is then improved by fusion strategy that selects the energy corresponding to the lowest uncertainty. As a sequence, the hyperspectral image classification task stated in term of energy minimization is efficiently solved by graph cuts algorithm. Experiments on two real hyperspectral data sets are provided to demonstrate the effectiveness of our hyperspectral classification algorithm.