Hyperspectral unmixing using total variation regularized reweighted sparse non-negative matrix factorization

Abstract

Recently, non-negative matrix factorization (NMF) model has been widely used in hyperspectral unmixing (HU). In this paper, based on NMF, we explore the properties of abundance maps, and propose a new blind HU algorithm named total variation regularized reweighted sparse NMF (TV-RSNMF). Typically, only a subset of endmembers are assumed to generate the fixed pixel. As a result, the abundance maps are assumed to be sparse. So we introduce a weighted sparse regularization to explore the sparsity of abundance maps in the NMF model. In addition, the abundance maps related to fixed material are assumed to be piecewise smooth and we adopt a total variation (TV) regularizer to promote the piecewise smooth property. TV regularizer can be regarded as an abundance maps denoising procedure, which significantly improves the robustness of the proposed method to noise. Several experiments were conducted to illustrate the performance of the proposed algorithm.