Hyperspectral Images Unmixing Based on Abundance Constrained Multi-Layer KNMF

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.

Automated summary

A novel nonlinear unmixing method, AC-MLKNMF, has been proposed for hyperspectral image analysis, which outperforms other methods in terms of unmixing accuracy and provides more accurate results based on the spatial distribution characteristics of actual ground objects.