Sketch-Based Region Adaptive Sparse Unmixing Applied to Hyperspectral Image

Hyperspectral image (HSI) unmixing is an important issue of research due to its effect on the subsequent processing of HSIs. Recently, the sparse regression method with spatial information has been successfully applied in hyperspectral unmixing (HU). However, most sparse regression methods ignore the difference in spatial structure handling with only one sparse constraint. In fact, the pixels in detail regions are more likely to be severely mixed with more endmembers participated, and the sparsity degree of its corresponding abundances is relatively low. Considering the sparsity difference of abundances, a sketch-based region adaptive sparse unmixing applied to HSI is proposed in this article. Inspired by the vision computing theory, we use the region generation algorithm based on a sketch map to differentiate the homogeneous regions and detail regions. Then, the abundances of these two kind regions in HSIs are separately constrained by sparse regularizers of L-1/2 and L-1 with a proposed manifold constraint. Our method not only makes full use of the spatial information in HSIs but also exploits the latent structure of data. The encouraging experimental results on three data sets validate the effectiveness of our method for HU.