Blind spectral unmixing for compressive hyperspectral imaging of highly mixed data

A novel method for blind spectral unmixing directly from compressive measurements of highly mixed hyperspectral data is presented. Unlike existing unmixing algorithms in compressed sensing (CS), our method does not assume the dominant presence of pure pixels in the underlying data. Our approach brings together multiple important priors in the hyperspectral data by penalizing the TV norm of the abundances, the variance of the endmembers as well as the geometric distances between them. The solution therefore simultaneously accounts for the internal characteristics within each material constituting the underlying data, and the external geometry between the materials under the linear mixing model. Experimental results over noisy CS measurements with highly mixed data demonstrate the effectiveness of our approach over existing methods.