Hyperspectral Images Unmixing Based on Abundance Constrained Multi-Layer KNMF

Abstract

Due to the low spatial resolution of the sensors, the hyperspectral images contain mixed pixels. The purpose of hyperspectral unmixing is to decompose the mixed pixels into a series of endmembers and abundance fractions. In order to improve the performance of the nonlinear unmixing algorithm for hyperspectral images, a nonlinear unmixing method, i.e., abundance constrained multi-layer kernel non-negative matrix factorization (AC-MLKNMF), is presented. Firstly, MLKNMF is presented to iteratively decompose the mixed pixels into a multi-layer structure, and then AC-MLKNMF is presented based on MLKNMF by adding the sparseness constraint and total variation regularization to the abundance to characterize the sparseness and the piecewise smooth structure of the abundance maps according to the spatial distribution characteristics of the actual ground-objects. Experimental results on synthetic and real datasets show that the proposed AC-MLKNMF can improve the hyperspectral unmixing accuracy compared with single-layer KNMF, and it is also superior to multi-layer non-negative matrix factorization, KNMF without pure pixels, kernel sparse NMF, MLKNMF.INDEX TERMS Multi-layer kernel non-negative matrix factorization (MLKNMF), abundance constrained, hyperspectral data, mixed pixels.

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

“…Here, the endmember and abundance matrices are A = A L and S = S L • • • S 2 S 1 , respectively. Furthermore, multilayer factorization was investigated with fast kernel archetypal analysis (KAA) [157] and kernel NMF [158] for unmixing. However, these models are optimized by only minimizing the cost function of each layer, which fail to reduce the total reconstruction error.…”