Task-Oriented Unsupervised SAR Image Enhancement with Semantic Preservation for Robust Target Recognition.
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| Title: | Task-Oriented Unsupervised SAR Image Enhancement with Semantic Preservation for Robust Target Recognition. |
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| Authors: | Wan, Chengyu1 (AUTHOR), Zhang, Siqian1 (AUTHOR) zhangsiqian@nudt.edu.cn, Zhao, Lingjun1 (AUTHOR), Tang, Tao1 (AUTHOR), Kuang, Gangyao1 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p930. 25p. |
| Subjects: | Automatic target recognition, Image enhancement (Imaging systems), Machine learning, Generative adversarial networks |
| Abstract: | Highlights: What are the main findings? A novel unsupervised SAR image enhancement framework based on DualGAN is proposed, addressing the domain shift problem between low- and high-quality SAR images. Introduces a segmentation-guided recognition-oriented constraint (ROC) and a semantic preservation constraint (SPC) to enhance task-relevant feature preservation and reduce semantic drift during unpaired translation. What are the implication of the main finding? The proposed framework improves both image quality and target recognition accuracy in SAR applications, achieving an over 10% improvement in recognition accuracy across multiple networks. Highlights the importance of task-aware image enhancement in SAR applications, especially under conditions where paired high-quality reference data is unavailable. Synthetic aperture radar (SAR) images often suffer from coupled degradations such as speckle noise, background clutter, and system disturbances, which distort target structure and reduce feature discriminability for target recognition. Most existing enhancement methods typically optimize perceptual quality and may produce visually appealing yet recognition-inconsistent results, especially when paired supervision is unavailable. To address this, an unsupervised SAR image quality enhancement framework is proposed in this study, formulating the degradation as a domain shift problem between low- and high-quality SAR data. A DualGAN-based architecture is adopted to learn bidirectional mappings with reconstruction regularization, enabling enhancement without paired samples. To explicitly preserve task-relevant features and enforce structural consistency, a segmentation-guided recognition-oriented constraint is introduced to embed task awareness into the enhancement process. Furthermore, to mitigate semantic drift during unpaired translation, a semantic preservation constraint based on contrastive learning is proposed to align the enhanced, original, and smoothed images, which can maintain semantic fidelity and reinforce structural cues. Experimental results demonstrate that the proposed framework effectively bridges the domain gap between low- and high-quality SAR images, producing semantically consistent enhancement and improving robustness in target recognition. Evaluations on the GMVT dataset show that the proposed method achieves an average recognition accuracy improvement of over 10% across six recognition networks and four imaging conditions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A novel unsupervised SAR image enhancement framework based on DualGAN is proposed, addressing the domain shift problem between low- and high-quality SAR images. Introduces a segmentation-guided recognition-oriented constraint (ROC) and a semantic preservation constraint (SPC) to enhance task-relevant feature preservation and reduce semantic drift during unpaired translation. What are the implication of the main finding? The proposed framework improves both image quality and target recognition accuracy in SAR applications, achieving an over 10% improvement in recognition accuracy across multiple networks. Highlights the importance of task-aware image enhancement in SAR applications, especially under conditions where paired high-quality reference data is unavailable. Synthetic aperture radar (SAR) images often suffer from coupled degradations such as speckle noise, background clutter, and system disturbances, which distort target structure and reduce feature discriminability for target recognition. Most existing enhancement methods typically optimize perceptual quality and may produce visually appealing yet recognition-inconsistent results, especially when paired supervision is unavailable. To address this, an unsupervised SAR image quality enhancement framework is proposed in this study, formulating the degradation as a domain shift problem between low- and high-quality SAR data. A DualGAN-based architecture is adopted to learn bidirectional mappings with reconstruction regularization, enabling enhancement without paired samples. To explicitly preserve task-relevant features and enforce structural consistency, a segmentation-guided recognition-oriented constraint is introduced to embed task awareness into the enhancement process. Furthermore, to mitigate semantic drift during unpaired translation, a semantic preservation constraint based on contrastive learning is proposed to align the enhanced, original, and smoothed images, which can maintain semantic fidelity and reinforce structural cues. Experimental results demonstrate that the proposed framework effectively bridges the domain gap between low- and high-quality SAR images, producing semantically consistent enhancement and improving robustness in target recognition. Evaluations on the GMVT dataset show that the proposed method achieves an average recognition accuracy improvement of over 10% across six recognition networks and four imaging conditions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18060930 |
