Geometric Nonnegative Matrix Factorization (GNMF) for Hyperspectral Unmixing
Geometric Nonnegative Matrix Factorization (GNMF) for Hyperspectral Unmixing
复制标题
用于高光谱解混的几何非负矩阵分解 (GNMF)
DOI：
10.1109/jstars.2015.2417574
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发表时间：
2015
影响因子：
5.5
通讯作者：
Jiao Licheng
中科院分区：
文献类型：
--
作者：
Yang Shuyuan;Zhang Xiantong;Yao Yigang;Cheng Shiqian;Jiao Licheng
在现有的高光谱解混方法中，高光谱图像通常被视为没有几何组织的光谱测量列表。在本文中，我们探索的几何结构的高光谱图像在空间域和光谱域提出了几何非负矩阵分解（GNMF）更准确的端元提取和丰度估计。在建议的GNMF中，我们定义了“空间几何距离”和“光谱几何距离”来揭示像素之间的亲和力。在局部区域的高光谱矢量的空间几何均匀性，在光谱域的几何流形结构，探索制定空间光谱流形正则化NMF。在合成数据和真实的高光谱数据上进行了实验，研究了GNMF的性能，结果表明它可以提供最先进的混合解混结果。
In the available hyperspectral unmixing approaches, hyperspectral images are often treated as a list of spectral measurements with no geometric organization. In this paper, we explore the geometric structure of hyperspectral images in both the spatial domain and the spectral domain to advance a geometric nonnegative matrix factorization (GNMF) for more accurate endmember extraction and abundance estimation. In the proposed GNMF, we define the “spatial geometric distance” and “spectral geometric distance” to reveal the affinity between pixels. Both the spatial geometric homogeneity of hyperspectral vectors in a local region, and the geometry manifold structure in the spectral domain, are explored to formulate spatial-spectral manifold regularizer for NMF. Some experiments are taken on some synthetic data and real hyperspectral data to investigate the performance of GNMF, and the results show that it can present state-of-the-art unmixing results.