Spatial Discontinuity-Weighted Sparse Unmixing of Hyperspectral Images

Abstract

Spectral unmixing is an important technique for remotely sensed hyperspectral image interpretation, of which the goal is to decompose the image into a set of pure spectral components (endmembers) and their abundance fractions in each pixel of the scene. Sparse-representation-based approaches have been widely studied for remotely sensed hyperspectral unmixing. A recent trend is to incorporate the spatial information to improve the spectral unmixing results. Those methods generally assume that the abundances of the pixels are piecewise smooth and fall into a homogeneous region occupied by the same endmembers and their corresponding fractional abundances. However, in real scenarios, abundances may vary abruptly from pixel to pixel. Therefore, the former assumption in most spatial models does not hold. To address this limitation, we propose a new strategy to preserve the spatial details in the abundance maps via a spatial discontinuity weight. Our experimental results, conducted with both simulated and real hyperspectral data sets, illustrate the good potential of our discontinuity-preserving strategy for sparse unmixing, which can greatly improve the abundance estimation results.