Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain Learning
Existing deep learning-based hyperspectral anomaly detection methods often overlook frequency domain features, hindering the ability to effectively distinguish between background and anomalies. Furthermore, many methods directly apply Mahalanobis distance to reconstructed hyperspectral image (HSI) f...
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| Format: | Article |
| Language: | English |
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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/11038935/ |
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| _version_ | 1850085429003419648 |
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| author | Xiaoyi Wang Jun Du Qingxuan Lv Peng Wang Xianchao Zhang Jian Cheng Henry Leung |
| author_facet | Xiaoyi Wang Jun Du Qingxuan Lv Peng Wang Xianchao Zhang Jian Cheng Henry Leung |
| author_sort | Xiaoyi Wang |
| collection | DOAJ |
| description | Existing deep learning-based hyperspectral anomaly detection methods often overlook frequency domain features, hindering the ability to effectively distinguish between background and anomalies. Furthermore, many methods directly apply Mahalanobis distance to reconstructed hyperspectral image (HSI) for detection, disregarding the structural features of the original HSI. This leads to insufficient representation of important information and ultimately limits detection performance. To resolve these challenges, this article presents a dual-branch network based on frequency domain learning. Using the Haar wavelet transform, the original HSI is divided into high- and low-frequency components. Based on the edge detailed properties of the high-frequency component and the smoothness of the low-frequency component, distinct network branches are designed for feature extraction, with the extracted features fused for HSI reconstruction. Notably, the detection process utilizes the outputs of the high- and low-frequency branches directly, rather than the reconstructed HSI. To fully leverage the key structural information in the original HSI, a Mahalanobis distance detection method incorporating the structural similarity index is proposed. By weighting the covariance matrix, the method enhances critical structural and spectral features, improving detection accuracy while effectively suppressing noise. Experiments on six datasets demonstrate the proposed method’s superiority and robustness over eight advanced hyperspectral anomaly detection methods. |
| format | Article |
| id | doaj-art-7791643cdc97474f92aa03868660e36c |
| institution | DOAJ |
| 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-7791643cdc97474f92aa03868660e36c2025-08-20T02:43:43ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118157891580310.1109/JSTARS.2025.358057511038935Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain LearningXiaoyi Wang0https://orcid.org/0000-0002-1010-0158Jun Du1https://orcid.org/0000-0001-8576-1041Qingxuan Lv2Peng Wang3https://orcid.org/0000-0002-3825-6365Xianchao Zhang4https://orcid.org/0000-0001-8925-8371Jian Cheng5Henry Leung6https://orcid.org/0000-0002-5984-107XJoint Laboratory of Spatial Intelligent Perception and Large Model Application, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaJoint Laboratory of Spatial Intelligent Perception and Large Model Application, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaJoint Laboratory of Spatial Intelligent Perception and Large Model Application, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaJoint Laboratory of Spatial Intelligent Perception and Large Model Application, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaProvincial Key Laboratory of Multimodal Perceiving and Intelligent Systems, Jiaxing University, Jiaxing, ChinaJoint Laboratory of Spatial Intelligent Perception and Large Model Application, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaDepartment of Electrical and Computer Engineering, University of Calgary, Calgary, CanadaExisting deep learning-based hyperspectral anomaly detection methods often overlook frequency domain features, hindering the ability to effectively distinguish between background and anomalies. Furthermore, many methods directly apply Mahalanobis distance to reconstructed hyperspectral image (HSI) for detection, disregarding the structural features of the original HSI. This leads to insufficient representation of important information and ultimately limits detection performance. To resolve these challenges, this article presents a dual-branch network based on frequency domain learning. Using the Haar wavelet transform, the original HSI is divided into high- and low-frequency components. Based on the edge detailed properties of the high-frequency component and the smoothness of the low-frequency component, distinct network branches are designed for feature extraction, with the extracted features fused for HSI reconstruction. Notably, the detection process utilizes the outputs of the high- and low-frequency branches directly, rather than the reconstructed HSI. To fully leverage the key structural information in the original HSI, a Mahalanobis distance detection method incorporating the structural similarity index is proposed. By weighting the covariance matrix, the method enhances critical structural and spectral features, improving detection accuracy while effectively suppressing noise. Experiments on six datasets demonstrate the proposed method’s superiority and robustness over eight advanced hyperspectral anomaly detection methods.https://ieeexplore.ieee.org/document/11038935/Frequency domain decompositionmultiscale convolutionstructural similarity index (SSIM)-weighted Mahalanobis distancetransformer |
| spellingShingle | Xiaoyi Wang Jun Du Qingxuan Lv Peng Wang Xianchao Zhang Jian Cheng Henry Leung Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain Learning IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Frequency domain decomposition multiscale convolution structural similarity index (SSIM)-weighted Mahalanobis distance transformer |
| title | Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain Learning |
| title_full | Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain Learning |
| title_fullStr | Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain Learning |
| title_full_unstemmed | Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain Learning |
| title_short | Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain Learning |
| title_sort | hyperspectral anomaly detection using dual branch network based on frequency domain learning |
| topic | Frequency domain decomposition multiscale convolution structural similarity index (SSIM)-weighted Mahalanobis distance transformer |
| url | https://ieeexplore.ieee.org/document/11038935/ |
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