HSFMamba: Hierarchical Selective Fusion Mamba Network for Optics-Guided Joint Super-Resolution and Denoising of Noise-Corrupted SAR Images
Synthetic Aperture Radar (SAR) image interpretation has attracted widespread attention in remote sensing applications. However, the performance of existing methods is severely hindered by inherent limitations of SAR imaging mechanisms, such as speckle noise and low resolution. With the continuous ad...
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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/11045185/ |
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| author | Zhicheng Zhao Qing Gao Jinquan Yan Chenglong Li Jin Tang |
| author_facet | Zhicheng Zhao Qing Gao Jinquan Yan Chenglong Li Jin Tang |
| author_sort | Zhicheng Zhao |
| collection | DOAJ |
| description | Synthetic Aperture Radar (SAR) image interpretation has attracted widespread attention in remote sensing applications. However, the performance of existing methods is severely hindered by inherent limitations of SAR imaging mechanisms, such as speckle noise and low resolution. With the continuous advancement of remote sensing, it has become increasingly feasible to simultaneously acquire optical and SAR images. Given rich details in optical images, it is crucial to exploit this valuable information to guide quality enhancement of SAR images, thereby significantly improving their performance for practical applications. In this work, we propose a novel Hierarchical Selective Fusion Mamba Network (HSFMamba) for optics-guided joint super-resolution and denoising of SAR images, which simultaneously addresses resolution limitations and noise corruption in a unified framework. HSFMamba leverages the long-range modeling capability of the state space model with linear complexity and incorporates optical images through two progressive cross-selection scan mechanisms to perform high-quality reconstruction of SAR images corrupted by speckle noise. Specifically, we design a cross-modal feature selection module that dynamically identifies significant representations in optical images, thereby progressively extracting key information. To further leverage optical details while mitigating SAR speckle noise, we develop a frequency-spatial adaptive aggregation module aimed at better restoring image details, effectively enhancing critical high-frequency information. Additionally, we construct a well-aligned and high-resolution dataset for optics-guided joint SAR image super-resolution and denoising, comprising 3,200 optical-SAR image pairs, totaling 25,600 pairs across eight degradation modes. Extensive experiments demonstrate that HSFMamba effectively utilizes optical information to improve SAR image quality, outperforming several state-of-the-art methods. |
| format | Article |
| id | doaj-art-05dc4d551bd64b2aa58b1f4167e0b8b7 |
| 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-05dc4d551bd64b2aa58b1f4167e0b8b72025-08-20T03:17:46ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118164451646110.1109/JSTARS.2025.358121611045185HSFMamba: Hierarchical Selective Fusion Mamba Network for Optics-Guided Joint Super-Resolution and Denoising of Noise-Corrupted SAR ImagesZhicheng Zhao0https://orcid.org/0000-0002-2761-7399Qing Gao1Jinquan Yan2Chenglong Li3https://orcid.org/0000-0002-7233-2739Jin Tang4https://orcid.org/0000-0001-8375-3590Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, ChinaAnhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, ChinaInformation Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, ChinaInformation Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, ChinaAnhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, ChinaSynthetic Aperture Radar (SAR) image interpretation has attracted widespread attention in remote sensing applications. However, the performance of existing methods is severely hindered by inherent limitations of SAR imaging mechanisms, such as speckle noise and low resolution. With the continuous advancement of remote sensing, it has become increasingly feasible to simultaneously acquire optical and SAR images. Given rich details in optical images, it is crucial to exploit this valuable information to guide quality enhancement of SAR images, thereby significantly improving their performance for practical applications. In this work, we propose a novel Hierarchical Selective Fusion Mamba Network (HSFMamba) for optics-guided joint super-resolution and denoising of SAR images, which simultaneously addresses resolution limitations and noise corruption in a unified framework. HSFMamba leverages the long-range modeling capability of the state space model with linear complexity and incorporates optical images through two progressive cross-selection scan mechanisms to perform high-quality reconstruction of SAR images corrupted by speckle noise. Specifically, we design a cross-modal feature selection module that dynamically identifies significant representations in optical images, thereby progressively extracting key information. To further leverage optical details while mitigating SAR speckle noise, we develop a frequency-spatial adaptive aggregation module aimed at better restoring image details, effectively enhancing critical high-frequency information. Additionally, we construct a well-aligned and high-resolution dataset for optics-guided joint SAR image super-resolution and denoising, comprising 3,200 optical-SAR image pairs, totaling 25,600 pairs across eight degradation modes. Extensive experiments demonstrate that HSFMamba effectively utilizes optical information to improve SAR image quality, outperforming several state-of-the-art methods.https://ieeexplore.ieee.org/document/11045185/Denoisingmambamultimodaloptics-guidedremote sensingSynthetic aperture radar (SAR) image super-resolution (SR) |
| spellingShingle | Zhicheng Zhao Qing Gao Jinquan Yan Chenglong Li Jin Tang HSFMamba: Hierarchical Selective Fusion Mamba Network for Optics-Guided Joint Super-Resolution and Denoising of Noise-Corrupted SAR Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Denoising mamba multimodal optics-guided remote sensing Synthetic aperture radar (SAR) image super-resolution (SR) |
| title | HSFMamba: Hierarchical Selective Fusion Mamba Network for Optics-Guided Joint Super-Resolution and Denoising of Noise-Corrupted SAR Images |
| title_full | HSFMamba: Hierarchical Selective Fusion Mamba Network for Optics-Guided Joint Super-Resolution and Denoising of Noise-Corrupted SAR Images |
| title_fullStr | HSFMamba: Hierarchical Selective Fusion Mamba Network for Optics-Guided Joint Super-Resolution and Denoising of Noise-Corrupted SAR Images |
| title_full_unstemmed | HSFMamba: Hierarchical Selective Fusion Mamba Network for Optics-Guided Joint Super-Resolution and Denoising of Noise-Corrupted SAR Images |
| title_short | HSFMamba: Hierarchical Selective Fusion Mamba Network for Optics-Guided Joint Super-Resolution and Denoising of Noise-Corrupted SAR Images |
| title_sort | hsfmamba hierarchical selective fusion mamba network for optics guided joint super resolution and denoising of noise corrupted sar images |
| topic | Denoising mamba multimodal optics-guided remote sensing Synthetic aperture radar (SAR) image super-resolution (SR) |
| url | https://ieeexplore.ieee.org/document/11045185/ |
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