Abstract
Hyperspectral image unmixing is an important task for remote sensing image processing. It aims at decomposing the mixed pixel of the image to identify a set of constituent materials called endmembers and to obtain their proportions named abundances. Recently, number of algorithms based on sparse nonnegative matrix factorization (NMF) have been widely used in hyperspectral unmixing with good performance. However, these sparse NMF algorithms only consider the correlation characteristics of abundance and usually just take the Euclidean structure of data into account, which can make the extracted endmembers become inaccurate. Therefore, with the aim of addressing this problem, we present a sparse NMF algorithm based on endmember independence and spatial weighted abundance in this paper. Firstly, it is assumed that the extracted endmembers should be independent from each other. Thus, by utilizing the autocorrelation matrix of endmembers, the constraint based on endmember independence is to be constructed in the model. In addition, two spatial weights for abundance by neighborhood pixels and correlation coefficient are proposed to make the estimated abundance smoother so as to further explore the underlying structure of hyperspectral data. The proposed algorithm not only considers the relevant characteristics of endmembers and abundances simultaneously, but also makes full use of the spatial-spectral information in the image, achieving a more desired unmixing performance. The experiment results on several data sets further verify the effectiveness of the proposed algorithm.
Highlights
Inspired by the advantage of nonnegative matrix factorization (NMF) model, we develop a sparse NMF unmixing algorithm based on endmember independence and spatial weighted abundance for Hyperspectral image (HSI)
The results of the EASNMF algorithm and the comparisons composed of minimumvolume constraint NMF (MVCNMF), L1/2 -NMF, GLNMF and collaborative NMF (CoNMF) on both the simulated data set and the real data set are displayed and analyzed
Owing to the appropriate constraints based on the endmember independence and spatial weight, the performance of the proposed method is slightly higher than the listed comparison algorithms for unmixing
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Lagrangian (SUnSAL) [19] algorithm explores the L1 norm as the constraint for abundance based on the alternating direction method of multipliers to solve the sparse regression problem It just analyzes the hyperspectral data and does not incorporate the spatial information. Inspired by the advantage of NMF model, we develop a sparse NMF unmixing algorithm based on endmember independence and spatial weighted abundance for HSI (EASNMF). The proposed sparse unmixing algorithm based on endmember independence and spatial weighted abundance with manifold regularization is introduced in detail. The raised EASNMF algorithm can get the independent endmembers and the smooth abundances, which fully exploits the spatial-spectral information and the intrinsic geometrical characteristics of HSI data
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