IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium | 2019
Hyperspectral Unmixing VIA L1/4 Sparsity-Constrained Multilayer NMF
Abstract
Hyperspectral unmixing, by extracting the fractional abundances of endmembers from the hyperspectral image (HSI), has raised wide attention in recent years. In last decade, nonnegative matrix factorization (NMF) have been intensively studied for solving spectral unmixing problem. In this paper, we extend the multilayer NMF method by incorporating the L1/4 sparsity constraint, named L1/4-MLNMF. The L1/4 regularizer induces sparsity effectively. We propose an iterative estimation algorithm for L1/4-MLNMF, which provides sparser and more accurate results than MLNMF. Experiments on a synthetic dataset and a real dataset show that the prposed method outperforms the similar competitors.