Blind spatial unmixing of multispectral images: An approach based on two-source sparsity and geometrical properties

Due to the limited spatial resolution of some remote sensing sensors, their image pixel spectra are commonly mixtures of elementary contributions. To analyze this type of images, it is necessary for some applications to perform spectral unmixing. This procedure allows the decomposition of a mixed pixel spectrum into a set of pure material spectra, and a set of abundance fractions. To this end, we here propose a new unsupervised spatial Blind Source Separation approach based on sparsity and geometrical properties. This approach first consists in finding small zones (composed of several adjacent pixels) containing only two sources using a spatial correlation-based method. This stage is followed by an identification stage where we geometrically estimate the pure material spectra. The final stage is the estimation of the searched abundances using a non-negative least squares method. The results obtained for simulated mixtures of realistic sources show the good performance of our method.