Sparse constrained low tensor rank representation framework for hyperspectral unmixing | |
Dong, Le2,3; Yuan, Yuan1 | |
刊名 | Remote Sensing |
2021-04-02 | |
卷号 | 13期号:8 |
关键词 | hyperspectral unmixing low tensor rank non-negative tensor factorization sparse constraint |
ISSN号 | 20724292 |
DOI | 10.3390/rs13081473 |
产权排序 | 1 |
英文摘要 | Recently, non-negative tensor factorization (NTF) as a very powerful tool has attracted the attention of researchers. It is used in the unmixing of hyperspectral images (HSI) due to its excellent expression ability without any information loss when describing data. However, most of the existing unmixing methods based on NTF fail to fully explore the unique properties of data, for example, low rank, that exists in both the spectral and spatial domains. To explore this low-rank structure, in this paper we learn the different low-rank representations of HSI in the spectral, spatial and non-local similarity modes. Firstly, HSI is divided into many patches, and these patches are clustered multiple groups according to the similarity. Each similarity group can constitute a 4-D tensor, including two spatial modes, a spectral mode and a non-local similarity mode, which has strong low-rank properties. Secondly, a low-rank regularization with logarithmic function is designed and embedded in the NTF framework, which simulates the spatial, spectral and non-local similarity modes of these 4-D tensors. In addition, the sparsity of the abundance tensor is also integrated into the unmixing framework to improve the unmixing performance through the L2,1 norm. Experiments on three real data sets illustrate the stability and effectiveness of our algorithm compared with five state-of-the-art methods. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
语种 | 英语 |
出版者 | MDPI AG |
WOS记录号 | WOS:000644676100001 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/94694] |
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.School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an; 710072, China 2.University of Chinese Academy of Sciences, Beijing; 100049, China; 3.Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an; 710119, China; |
推荐引用方式 GB/T 7714 | Dong, Le,Yuan, Yuan. Sparse constrained low tensor rank representation framework for hyperspectral unmixing[J]. Remote Sensing,2021,13(8). |
APA | Dong, Le,&Yuan, Yuan.(2021).Sparse constrained low tensor rank representation framework for hyperspectral unmixing.Remote Sensing,13(8). |
MLA | Dong, Le,et al."Sparse constrained low tensor rank representation framework for hyperspectral unmixing".Remote Sensing 13.8(2021). |
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