Distributed Blind Hyperspectral Unmixing via Joint Sparsity and Low-Rank Low Constrained Non Non-Negative Matrix Factorization
Abstract: Hyperspectral unmixing is a crucial processing step in remote sensing image analysis. Its aim is the decomposition of each pixel in a hyperspectral image into a number of materials, the so so-called called endmembers, and their corresponding abundance fractions. Among the various unmixin unmixingg approaches that have been suggested in the literature, we are interested here in unsupervised techniques that rely on some form of non non-negative negative Matrix factorization (NMF). NMF-based NMF techniques provide an easy way to simultaneously estimate the endmembers and their corresponding abundances, though they suffer from mediocre performance and high computational complexity due to the nonconvexity of the involved cost function. Improvements in performance have been recently achieved by imposing additional constraints aints to the NMF optimization problem related to the sparsity of the abundances. Another feature of hyperspectral images that can be exploited is their high spatial correlation, which is translated into the low rank of the involved abundance matrices. Moti Motivated vated by this, in this paper we propose a novel unmixing method that is based on a simultaneously sparse and low-rank low constrained NMF. In addition, prompted by the rapid evolution of multicore processors and graphics processing units, we devise a distributed distribut unmixing scheme that processes in parallel different parts of the image. The proposed distributed unmixing algorithm achieves improved performance and faster