期刊论文详细信息
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | |
Spectral-Spatial Constrained Nonnegative Matrix Factorization for Spectral Mixture Analysis of Hyperspectral Images | |
Ge Zhang1  Yan Feng1  Qian Du2  Shaohui Mei2  | |
[1]School of Electronics and Information, Northwestern Polytechnical University, Xi&x2019 | |
[2]an, China | |
关键词: Hyperspectral remote sensing; linear mixture model (LMM); nonnegative matrix factorization (NMF); spectral mixture analysis (SMA); | |
DOI : 10.1109/JSTARS.2021.3092566 | |
来源: DOAJ |
【 摘 要 】
Hyperspectral spectral mixture analysis (SMA), which intends to decompose mixed pixels into a collection of endmembers weighted by their corresponding fraction abundances, has been successfully used to tackle mixed-pixel problem in hyperspectral remote sensing applications. As an approach of decomposing a high-dimensional data matrix into the multiplication of two nonnegative matrices, nonnegative matrix factorization (NMF) has shown its advantages and been widely applied to SMA. Unfortunately, most of the NMF-based unmixing methods can easily lead to an unsuitable solution, due to inadequate mining of spatial and spectral information and the influence of outliers and noise. To overcome such limitations, a spatial constraint over abundance and a spectral constraint over endmembers are imposed over NMF-based unmixing model for spectral-spatial constrained unmixing. Specifically, a spatial neighborhood preserving constraint is proposed to preserve the local geometric structure of the hyperspectral data by assuming that pixels in a spatial neighborhood generally fall into a low-dimensional manifold, while a minimum spectral distance constraint is formulated to optimize endmember spectra as compact as possible. Furthermore, to handle non-Gaussian noises or outliers, an【 授权许可】
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