A Novel NMF Guided for Hyperspectral Unmixing From Incomplete and Noisy Data | |
Dong, Le2; Lu, Xiaoqiang1; Liu, Ganchao2; Yuan, Yuan2 | |
刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
2022 | |
卷号 | 60 |
关键词 | Image reconstruction Hyperspectral imaging Noise measurement Gaussian noise Interference Stability analysis Sensors Hyperspectral unmixing (HU) image reconstruction nonnegative matrix factorization (NMF) spatial-spectral information |
ISSN号 | 0196-2892;1558-0644 |
DOI | 10.1109/TGRS.2021.3101504 |
产权排序 | 2 |
英文摘要 | The nonnegative matrix factorization (NMF)-combined spatial-spectral information has been widely applied in the unmixing of hyperspectral images (HSIs). However, how to select the appropriate similarity pixels and explore the spatial information and how to adapt the unmixing algorithm to complex data are both great challenges. In this article, we propose a novel unmixing method named spatial-spectral neighborhood preserving NMF (SSNPNMF) for incomplete and noisy HSI data. First, a spatial-spectral kernel regularizer is introduced to preprocess the HSI, which can reduce noise and complete missing elements. Second, a distance metric SSD based on spatial-spectral information is designed to select similar pixels in the image. Subsequently, the spatial-spectral relationship of the selected first k similar pixels is used to reconstruct the image and obtain the reconstruction matrix. Finally, the reconstruction matrix is used to constrain the abundances and improve the unmixing performance. Experimental results on synthetic data and Cuprite data indicate that SSNPNMF has a more effective unmixing performance compared with the state-of-the-art methods. |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000732810700001 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/95608] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Yuan, Yuan |
作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech 2.Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN |
推荐引用方式 GB/T 7714 | Dong, Le,Lu, Xiaoqiang,Liu, Ganchao,et al. A Novel NMF Guided for Hyperspectral Unmixing From Incomplete and Noisy Data[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60. |
APA | Dong, Le,Lu, Xiaoqiang,Liu, Ganchao,&Yuan, Yuan.(2022).A Novel NMF Guided for Hyperspectral Unmixing From Incomplete and Noisy Data.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60. |
MLA | Dong, Le,et al."A Novel NMF Guided for Hyperspectral Unmixing From Incomplete and Noisy Data".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022). |
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