Parallel adaptive sparsity-constrained NMF algorithm for hyperspectral unmixing

Abstract

Sparsity-constrained Nonnegative matrix factorization (NMF) has been proved to be an effective method for hyperspectral unmixing. However, the optimization procedure of sparsity-constrained NMF is computational demanding, which may limit its application in time-constrained conditions. In this paper, a parallel L 1/2 sparsity-constrained NMF unmixing method on Graphics Processing Units (GPUs) is proposed, and implemented using the Compute Unified Device Architecture (CUDA). It mainly involves the parallelization of multiplicative update rule for endmembers extraction and half thresholding update rule with adaptive regularization parameter strategy for abundance estimation. In particular, the concurrent kernel computation power of modern GPUs is employed to overlap the separated subtasks. The experiment results on the synthetic and real hyperspectral data demonstrate the effectiveness of our implementation.