Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective
In data acquisition and transmission, hyperspectral images are inevitably corrupted by additive noises, making it challenging to accurately observe and recognize the materials on the surface of the Earth. However, scholars have been addicted to developing numerous complex methods for separable two-s...
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IEEE
2024-01-01
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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/10621578/ |
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| author | Minghua Wang Lianru Gao Longfei Ren Xian Sun Jocelyn Chanussot |
| author_facet | Minghua Wang Lianru Gao Longfei Ren Xian Sun Jocelyn Chanussot |
| author_sort | Minghua Wang |
| collection | DOAJ |
| description | In data acquisition and transmission, hyperspectral images are inevitably corrupted by additive noises, making it challenging to accurately observe and recognize the materials on the surface of the Earth. However, scholars have been addicted to developing numerous complex methods for separable two-stage denoising and anomaly detection (AD) tasks over the past years, rarely paying attention to the real effect of noises for subsequent intelligent interpretation. To this date, we propose a hierarchical integration framework for hyperspectral simultaneous AD and denoising (HyADD). Joint AD and denoising are mutually integrated and their outputs in each iteration stimulate each other, breaking through the respective performance bottlenecks of the separable two-stage scheme. Inspired by spatial–spectral gradient domain-based constraint, HyADD removes additive noises and preserves advantageous image smoothness information to improve intermediate detection performances in the iteration loop. Conversely, with the assistance of the antinoise dictionary conduction and the subspace domain-based low-rankness, the identification of anomalies with different features from the background can provide effective feedback to the denoising process. The proposed algorithm is efficiently solved by the well-designed linearized alternating direction method of multipliers with an adaptive penalty. A comparison with the existing well-known AD algorithms via simulated and real-world experiments establishes the competitiveness of the proposed HyADD with state-of-the-art methods. |
| format | Article |
| id | doaj-art-239dc3e2eef74046bfa588ed17a411b5 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-239dc3e2eef74046bfa588ed17a411b52025-08-20T02:06:20ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117139661398010.1109/JSTARS.2024.343746010621578Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative PerspectiveMinghua Wang0https://orcid.org/0000-0001-5715-130XLianru Gao1https://orcid.org/0000-0003-3888-8124Longfei Ren2https://orcid.org/0000-0002-0414-5114Xian Sun3https://orcid.org/0000-0002-0038-9816Jocelyn Chanussot4https://orcid.org/0000-0003-4817-2875Institute of Robotics and Automatic Information System (IRAIS), College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics (tjKLIR), Nankai University, Tianjin, ChinaKey Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaCNRS, Grenoble INP, GIPSA-Lab, Université Grenoble Alpes, Grenoble, FranceIn data acquisition and transmission, hyperspectral images are inevitably corrupted by additive noises, making it challenging to accurately observe and recognize the materials on the surface of the Earth. However, scholars have been addicted to developing numerous complex methods for separable two-stage denoising and anomaly detection (AD) tasks over the past years, rarely paying attention to the real effect of noises for subsequent intelligent interpretation. To this date, we propose a hierarchical integration framework for hyperspectral simultaneous AD and denoising (HyADD). Joint AD and denoising are mutually integrated and their outputs in each iteration stimulate each other, breaking through the respective performance bottlenecks of the separable two-stage scheme. Inspired by spatial–spectral gradient domain-based constraint, HyADD removes additive noises and preserves advantageous image smoothness information to improve intermediate detection performances in the iteration loop. Conversely, with the assistance of the antinoise dictionary conduction and the subspace domain-based low-rankness, the identification of anomalies with different features from the background can provide effective feedback to the denoising process. The proposed algorithm is efficiently solved by the well-designed linearized alternating direction method of multipliers with an adaptive penalty. A comparison with the existing well-known AD algorithms via simulated and real-world experiments establishes the competitiveness of the proposed HyADD with state-of-the-art methods.https://ieeexplore.ieee.org/document/10621578/Anomaly detection (AD)denoisingfeedbackintegration frameworklinearized alternating direction method of multipliers |
| spellingShingle | Minghua Wang Lianru Gao Longfei Ren Xian Sun Jocelyn Chanussot Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Anomaly detection (AD) denoising feedback integration framework linearized alternating direction method of multipliers |
| title | Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective |
| title_full | Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective |
| title_fullStr | Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective |
| title_full_unstemmed | Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective |
| title_short | Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective |
| title_sort | hyperspectral simultaneous anomaly detection and denoising insights from integrative perspective |
| topic | Anomaly detection (AD) denoising feedback integration framework linearized alternating direction method of multipliers |
| url | https://ieeexplore.ieee.org/document/10621578/ |
| work_keys_str_mv | AT minghuawang hyperspectralsimultaneousanomalydetectionanddenoisinginsightsfromintegrativeperspective AT lianrugao hyperspectralsimultaneousanomalydetectionanddenoisinginsightsfromintegrativeperspective AT longfeiren hyperspectralsimultaneousanomalydetectionanddenoisinginsightsfromintegrativeperspective AT xiansun hyperspectralsimultaneousanomalydetectionanddenoisinginsightsfromintegrativeperspective AT jocelynchanussot hyperspectralsimultaneousanomalydetectionanddenoisinginsightsfromintegrativeperspective |