CGMSN: CFAR-Guided Mode-Selective Network for SAR Target Detection.
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| Title: | CGMSN: CFAR-Guided Mode-Selective Network for SAR Target Detection. |
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| Authors: | Yu, Lingjuan1 (AUTHOR), Xiong, Xinya1,2 (AUTHOR), Xie, Xiaochun2,3 (AUTHOR), Liang, Miaomiao1 (AUTHOR) liangmiaom@jxust.edu.cn, Yu, Xiangchun1,2 (AUTHOR), Jiao, Xuan1,3 (AUTHOR), Hong, Wen3 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p2040. 28p. |
| Subjects: | Constant false alarm rate (Data processing), Radar targets, Artificial neural networks |
| Abstract: | Highlights: What are the main findings? A CFAR anomaly response map is constructed, and a dataset-level target–background separation margin is introduced to characterize the separability between target regions and their surrounding contextual backgrounds. A mode-selective SAR target detection network with deep, medium, and shallow feature-fusion modes is designed, and the appropriate mode is selected according to the CFAR-guided target–background separation margin of each dataset. What are the implications of the main findings? The CFAR target–background separation margin provides an interpretable cue for associating SAR datasets with different feature-fusion depths in the detection network. Smaller separation margins indicate stronger target–background ambiguity and therefore motivate deeper fusion, whereas larger margins indicate clearer target–background separation and allow shallower fusion. The proposed CFAR-guided mode-selective network (CGMSN) integrates CFAR target–background separation margin estimation with a mode-selective detector, achieving superior detection performance on SAR-Aircraft-1.0, HRSID, and SSDD through enhanced contextual representation, interpretable feature-fusion mode selection, and CIoU-modulated classification-regression alignment. Improving detection performance across diverse synthetic aperture radar (SAR) scenes remains challenging because different datasets exhibit different levels of target–background separability. To address this issue, we propose a constant false alarm rate (CFAR)-guided mode-selective network (CGMSN), which selects an appropriate feature-fusion mode according to the CFAR target–background separation margin. Specifically, CFAR is used as an interpretable statistical tool to construct an anomaly response map. The separation margin is then calculated by comparing the average CFAR anomaly responses of annotated target regions and their surrounding contextual backgrounds. Based on this indicator, a You Only Look Once version 8 (YOLOv8)-based mode-selective detector is constructed with three key components. First, a lightweight representation-enhanced backbone that integrates ResNet18 and a dilated convolutional spatial pyramid (DCSP) module is adopted to improve contextual representation while maintaining moderate model complexity. Second, a mode-selective neck (MSN) is designed with three predefined fusion modes, where the appropriate fusion depth is selected according to the CFAR-guided target–background separation margin of each dataset. Third, a complete intersection over the union modulated head (CMH) is developed to enhance classification-regression alignment and suppress clutter-induced responses. Experiments on SAR-Aircraft-1.0, High-Resolution SAR Images Dataset (HRSID), and SAR Ship Detection Dataset (SSDD) indicate that datasets with smaller CFAR target–background separation margins benefit from deeper fusion, while datasets with larger separation margins can adopt shallower fusion. Moreover, the proposed CGMSN achieves superior performance over representative detectors, demonstrating its effectiveness on the evaluated SAR datasets with diverse scene characteristics. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A CFAR anomaly response map is constructed, and a dataset-level target–background separation margin is introduced to characterize the separability between target regions and their surrounding contextual backgrounds. A mode-selective SAR target detection network with deep, medium, and shallow feature-fusion modes is designed, and the appropriate mode is selected according to the CFAR-guided target–background separation margin of each dataset. What are the implications of the main findings? The CFAR target–background separation margin provides an interpretable cue for associating SAR datasets with different feature-fusion depths in the detection network. Smaller separation margins indicate stronger target–background ambiguity and therefore motivate deeper fusion, whereas larger margins indicate clearer target–background separation and allow shallower fusion. The proposed CFAR-guided mode-selective network (CGMSN) integrates CFAR target–background separation margin estimation with a mode-selective detector, achieving superior detection performance on SAR-Aircraft-1.0, HRSID, and SSDD through enhanced contextual representation, interpretable feature-fusion mode selection, and CIoU-modulated classification-regression alignment. Improving detection performance across diverse synthetic aperture radar (SAR) scenes remains challenging because different datasets exhibit different levels of target–background separability. To address this issue, we propose a constant false alarm rate (CFAR)-guided mode-selective network (CGMSN), which selects an appropriate feature-fusion mode according to the CFAR target–background separation margin. Specifically, CFAR is used as an interpretable statistical tool to construct an anomaly response map. The separation margin is then calculated by comparing the average CFAR anomaly responses of annotated target regions and their surrounding contextual backgrounds. Based on this indicator, a You Only Look Once version 8 (YOLOv8)-based mode-selective detector is constructed with three key components. First, a lightweight representation-enhanced backbone that integrates ResNet18 and a dilated convolutional spatial pyramid (DCSP) module is adopted to improve contextual representation while maintaining moderate model complexity. Second, a mode-selective neck (MSN) is designed with three predefined fusion modes, where the appropriate fusion depth is selected according to the CFAR-guided target–background separation margin of each dataset. Third, a complete intersection over the union modulated head (CMH) is developed to enhance classification-regression alignment and suppress clutter-induced responses. Experiments on SAR-Aircraft-1.0, High-Resolution SAR Images Dataset (HRSID), and SAR Ship Detection Dataset (SSDD) indicate that datasets with smaller CFAR target–background separation margins benefit from deeper fusion, while datasets with larger separation margins can adopt shallower fusion. Moreover, the proposed CGMSN achieves superior performance over representative detectors, demonstrating its effectiveness on the evaluated SAR datasets with diverse scene characteristics. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18122040 |
