Compressed sensing based hyperspectral unmixing

In hyperspectral images the measured spectra for each pixel can be modeled as convex combination of small number of end member spectra. Since the measured structure contains only a few of possible responses out of possibly many materials sparsity based convex optimization techniques or compressive sensing can be used for hyperspectral unmixing. In this work varying sparsity based techniques are tested for hyperspectral unmixing problem. Performance analysis of these techniques on sparsity level and measurement number are performed. Effect of high coherence of hyperspectral dictionaries is discussed and effect of signal to noise ratio is analyzed.