论文标题
基于非负矩阵分解的高光谱脉络化：全面审查
Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review
论文作者
论文摘要
高光谱脉冲是一项重要的技术，它估计了一组末日的成员及其相应的丰富度（HSI）。非负矩阵分解（NMF）在解决此问题方面起着越来越重要的作用。在本文中，我们介绍了针对高光谱不混合提出的基于NMF的方法的全面调查。以NMF模型为基准，我们通过利用HSI的主要特性（例如光谱，空间和结构信息）来展示如何改善NMF。我们将三个重要的开发方向分类，包括受限的NMF，结构化NMF和广义NMF。此外，进行了几项实验，以说明相关算法的有效性。最后，我们以可能的未来指示结束了这篇文章，目的是提供指南和灵感来促进高光谱脉络的发展。
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 the article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing.
