A Tensor-Based Go Decomposition Method for Hyperspectral Anomaly Detection
Hyperspectral anomaly detection (HAD) aims at effectively separating the anomaly target from the background. The low-rank and sparse matrix decomposition (LRaSMD) technique has shown great potential in HAD tasks. However, some LRaSMD models need to convert the hyperspectral data into a two-dimension...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10836889/ |
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| author | Meiping Song Xiao Zhang Lan Li Hongju Cao Haimo Bao |
| author_facet | Meiping Song Xiao Zhang Lan Li Hongju Cao Haimo Bao |
| author_sort | Meiping Song |
| collection | DOAJ |
| description | Hyperspectral anomaly detection (HAD) aims at effectively separating the anomaly target from the background. The low-rank and sparse matrix decomposition (LRaSMD) technique has shown great potential in HAD tasks. However, some LRaSMD models need to convert the hyperspectral data into a two-dimensional matrix. This cannot well maintain the characteristics of the hyperspectral image (HSI) in each dimension, thus degenerating its representation capacity. In this context, this article proposes a tensor-based Go decomposition (GODEC) model, called TGODEC. The TGODEC model supports the idea of GODEC, representing the HSI data as a combination of background tensor, anomaly tensor, and noise tensor. In detail, the background tensor is solved by the tensor singular value hard thresholding decomposition. The anomaly tensor is solved by a mapping matrix using the corresponding sparse cardinality. Interestingly, the obtained background and anomaly tensors can also be developed for HAD, thus a TGODEC-based anomaly detector is established, called TGODEC-AD. Specifically, the TGODEC-AD method combines the typical RX-AD and R-AD with the above decomposition result of the TGODEC model and constructs different modal operator detectors. Experimental results on multiple real hyperspectral datasets verify the effectiveness of the TGODEC and TGODEC-AD methods. It means that the proposed TGODEC model can effectively characterize the spatial structural features of HSI. As a result, the pure decomposed components can be obtained, contributing to detecting the anomaly target and suppressing the background better in HAD tasks. |
| format | Article |
| id | doaj-art-9a7356773d8a47608168b6f0fee6aad2 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-9a7356773d8a47608168b6f0fee6aad22025-02-05T00:00:10ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184584460010.1109/JSTARS.2025.352574310836889A Tensor-Based Go Decomposition Method for Hyperspectral Anomaly DetectionMeiping Song0https://orcid.org/0000-0002-4489-5470Xiao Zhang1https://orcid.org/0000-0001-5373-097XLan Li2https://orcid.org/0000-0002-3175-4169Hongju Cao3https://orcid.org/0000-0002-8725-4681Haimo Bao4https://orcid.org/0000-0001-5706-2155Information and Technology College, Dalian Maritime University, Dalian, ChinaInformation and Technology College, Dalian Maritime University, Dalian, ChinaInformation and Technology College, Dalian Maritime University, Dalian, ChinaSchool of Software, Dalian University of Foreign Languages, Dalian, ChinaSchool of Innovation Design, Guangzhou Academy of Fine Arts, Guangzhou, ChinaHyperspectral anomaly detection (HAD) aims at effectively separating the anomaly target from the background. The low-rank and sparse matrix decomposition (LRaSMD) technique has shown great potential in HAD tasks. However, some LRaSMD models need to convert the hyperspectral data into a two-dimensional matrix. This cannot well maintain the characteristics of the hyperspectral image (HSI) in each dimension, thus degenerating its representation capacity. In this context, this article proposes a tensor-based Go decomposition (GODEC) model, called TGODEC. The TGODEC model supports the idea of GODEC, representing the HSI data as a combination of background tensor, anomaly tensor, and noise tensor. In detail, the background tensor is solved by the tensor singular value hard thresholding decomposition. The anomaly tensor is solved by a mapping matrix using the corresponding sparse cardinality. Interestingly, the obtained background and anomaly tensors can also be developed for HAD, thus a TGODEC-based anomaly detector is established, called TGODEC-AD. Specifically, the TGODEC-AD method combines the typical RX-AD and R-AD with the above decomposition result of the TGODEC model and constructs different modal operator detectors. Experimental results on multiple real hyperspectral datasets verify the effectiveness of the TGODEC and TGODEC-AD methods. It means that the proposed TGODEC model can effectively characterize the spatial structural features of HSI. As a result, the pure decomposed components can be obtained, contributing to detecting the anomaly target and suppressing the background better in HAD tasks.https://ieeexplore.ieee.org/document/10836889/Go decompositionhyperspectral anomaly detection (HAD)low rank and sparse matrix decomposition (LRaSMD)singular value thresholdtensor representation |
| spellingShingle | Meiping Song Xiao Zhang Lan Li Hongju Cao Haimo Bao A Tensor-Based Go Decomposition Method for Hyperspectral Anomaly Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Go decomposition hyperspectral anomaly detection (HAD) low rank and sparse matrix decomposition (LRaSMD) singular value threshold tensor representation |
| title | A Tensor-Based Go Decomposition Method for Hyperspectral Anomaly Detection |
| title_full | A Tensor-Based Go Decomposition Method for Hyperspectral Anomaly Detection |
| title_fullStr | A Tensor-Based Go Decomposition Method for Hyperspectral Anomaly Detection |
| title_full_unstemmed | A Tensor-Based Go Decomposition Method for Hyperspectral Anomaly Detection |
| title_short | A Tensor-Based Go Decomposition Method for Hyperspectral Anomaly Detection |
| title_sort | tensor based go decomposition method for hyperspectral anomaly detection |
| topic | Go decomposition hyperspectral anomaly detection (HAD) low rank and sparse matrix decomposition (LRaSMD) singular value threshold tensor representation |
| url | https://ieeexplore.ieee.org/document/10836889/ |
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