Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions, including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude this article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing.

Automated summary

This article provides a comprehensive survey of NMF-based methods for hyperspectral unmixing. It explores how to improve NMF by utilizing the spectral, spatial, and structural information of hyperspectral images. The study categorizes the development directions into constrained NMF, structured NMF, and generalized NMF, and conducts experiments to demonstrate the effectiveness of associated algorithms. The article concludes with possible future directions for the development of hyperspectral unmixing.