Sparse representation based subpixel information extraction framework for hyperspectral remote sensing imagery

Sparse representation theory has become a powerful tool since it can obtain the sparsest or the unique solution for the underdetermined problem with the development of linear algebra, optimization, scientific computing and more. As subpixel information extraction encountered in hyperspectral remote sensing, which contains many mixed pixels, are famous under-determined ill-posed problem. In addition, there is no unified model to conquer the problems with the subpixel analysis techniques, i.e., spectral unmixing and subpixel mapping. To cope with this under-determined problem, a unified sparse subpixel information extraction framework was proposed in this paper, which connects sparse unmixing and sparse subpixel mapping methods in a unified theoretical system as a serious of sparse regression problem. The experimental results with hyperspectral images indicate that the proposed sparse representation framework outperforms the previous subpixel analysis approaches, hence, provides an effective option for subpixel information extraction idea for hyperspectral remote sensing imagery.