Hyperspectral Unmixing Using Nonlocal Similarity-Regularized Low-Rank Tensor Factorization | |
Yuan, Yuan3; Dong, Le1,2; Li, Xuelong3 | |
刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
2022 | |
卷号 | 60 |
关键词 | Tensors TV Hyperspectral imaging Data models Task analysis Optimization Data mining Hyperspectral unmixing low tensor rank nonlocal similarity nonnegative tensor factorization (NTF) |
ISSN号 | 0196-2892;1558-0644 |
DOI | 10.1109/TGRS.2021.3095488 |
产权排序 | 2 |
英文摘要 | Recently, methods based on nonnegative tensor factorization (NTF), which benefits from the tensor representation of hyperspectral imagery (HSI) without any information loss, have attracted increasing attention. However, most existing methods fail to explore the internal spatial structure of data, resulting in low unmixing performance. Moreover, when the algorithm is optimized, the solution is unstable. In this article, a regularizer based on nonlocal tensor similarity is proposed, which can not only fully preserve the global information of HSI but also mine the internal information of data in the spatial domain. HSI is regarded as a 3-D tensor and is directly subjected to endmember extraction and abundance estimation. To fully explore the structural characteristics of data, we simultaneously use the local smoothing and low tensor rank prior of the data to constrain the unmixing model. First, several 4-D tensor groups can be obtained after the nonlocal similarity structure of HSI is learned. Subsequently, a low tensor rank prior is applied to each 4-D tensor, which can fully simulate the nonlocal similarity in the image. In addition, total variation (TV) is also used to explore the local spatial relationship of data, which can generate a smooth abundance map through edge preservation. The optimization is solved by the ADMM algorithm. Experiments on synthetic and real data illustrate the superiority of the proposed method. |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000732753000001 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/95610] |
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.Univ Chinese Acad Sci 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech 3.Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN |
推荐引用方式 GB/T 7714 | Yuan, Yuan,Dong, Le,Li, Xuelong. Hyperspectral Unmixing Using Nonlocal Similarity-Regularized Low-Rank Tensor Factorization[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60. |
APA | Yuan, Yuan,Dong, Le,&Li, Xuelong.(2022).Hyperspectral Unmixing Using Nonlocal Similarity-Regularized Low-Rank Tensor Factorization.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60. |
MLA | Yuan, Yuan,et al."Hyperspectral Unmixing Using Nonlocal Similarity-Regularized Low-Rank Tensor Factorization".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022). |
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