Compressive sensing for endmember extraction

With constant improvement in spatial resolution of satellite sensors, the ability to eliminate the spectral secondary reflection effect is continuously enhanced, which brings new research opportunities for hyperspectral image classification, but mixed pixel decomposition of hyperspectral image has become a new research difficulty in the field of remote sensing due to issues such as the diversity of complex measured ground feature, multiple scattering of ground object spectra and real-time change, etc. This paper has carried out research on endmember extraction by making full use of sparsity of abundance coefficient vector in spectral library with compressed sensing theory as a research tool aimed at such issue such as low decomposition accuracy existed in hyperspectral unmixing process, redundant end members and slow processing speed etc. Experimental results can prove the proposed method is feasible