ConFAS-Net: Few-Shot SAR Target Recognition via Confusion-Aware Attention and Adaptive Decision Scaling.
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| Title: | ConFAS-Net: Few-Shot SAR Target Recognition via Confusion-Aware Attention and Adaptive Decision Scaling. |
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| Authors: | Zhao, Xin1 (AUTHOR), Xue, Xiaorong1 (AUTHOR) xr_986@163.com, Tian, Yishuo1 (AUTHOR), Yang, Jingtong1 (AUTHOR), Lu, Bingyan1 (AUTHOR), Zhang, Wen1 (AUTHOR), Wang, Wancheng1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1482. 21p. |
| Subjects: | Automatic target recognition, Feature extraction, Cost functions, Machine learning, Synthetic aperture radar, Artificial neural networks |
| Abstract: | Highlights: What are the main findings? We propose the ConFAS-Net model, which integrates three innovative modules—MS-CA multi-scale channel attention, CACL category-confusion-aware loss, and CADA category-adaptive decision adjustment—to systematically address the core issues of insufficient feature utilisation and severe category confusion in small-sample SAR target recognition. On the MSTAR dataset under 5/10/15/30-shot settings, the model achieved recognition accuracies of 73.25%, 87.43%, 94.97%, and 96.87%, respectively, representing a maximum improvement of 2.93 percentage points over baseline methods, whilst maintaining superior parameter efficiency and balancing accuracy with computational efficiency. What are the implications of the main findings? The establishment of a full-chain optimisation paradigm comprising 'feature enhancement—loss optimisation—decision adjustment' provides an innovative and practical technical solution for small-sample target recognition tasks. The model's lightweight design is tailored to the application requirements of resource-constrained scenarios, offering a viable approach for the engineering implementation of SAR target recognition under limited-sample conditions. Synthetic aperture radar (SAR) target recognition under few-shot scenarios faces challenges of insufficient feature extraction and severe inter-class confusion. To address these issues, a confusion-aware few-shot attention and scaling network (ConFAS-Net) is proposed. The method introduces a multi-scale channel attention module (MS-CA) to enhance adaptive extraction of multi-scale features, designs a confusion-aware loss optimization module (CACL) to guide discriminative feature learning using inter-class confusion information, and employs a class-adaptive decision adjustment module (CADA) to dynamically adjust classification boundaries for few-shot distribution characteristics. Extensive experiments on the standard MSTAR dataset demonstrated that ConFAS-Net achieved recognition accuracies of 73.25%, 87.43%, 94.97%, and 96.87% under 5-, 10-, 15-, and 30-shot settings, respectively. To rigorously substantiate the generalization capability and robustness of the proposed model across different data domains, additional validation was conducted on the public SAMPLE dataset, where ConFAS-Net consistently achieved state-of-the-art performance across all K-shot settings. Ablation studies and visualization analyses further validated the effectiveness of each proposed module. Comparisons with state-of-the-art methods demonstrate that the proposed method maintains high recognition accuracy while retaining a lightweight architecture of only 2.32 M parameters, providing an effective solution for SAR target recognition in resource-constrained environments. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We propose the ConFAS-Net model, which integrates three innovative modules—MS-CA multi-scale channel attention, CACL category-confusion-aware loss, and CADA category-adaptive decision adjustment—to systematically address the core issues of insufficient feature utilisation and severe category confusion in small-sample SAR target recognition. On the MSTAR dataset under 5/10/15/30-shot settings, the model achieved recognition accuracies of 73.25%, 87.43%, 94.97%, and 96.87%, respectively, representing a maximum improvement of 2.93 percentage points over baseline methods, whilst maintaining superior parameter efficiency and balancing accuracy with computational efficiency. What are the implications of the main findings? The establishment of a full-chain optimisation paradigm comprising 'feature enhancement—loss optimisation—decision adjustment' provides an innovative and practical technical solution for small-sample target recognition tasks. The model's lightweight design is tailored to the application requirements of resource-constrained scenarios, offering a viable approach for the engineering implementation of SAR target recognition under limited-sample conditions. Synthetic aperture radar (SAR) target recognition under few-shot scenarios faces challenges of insufficient feature extraction and severe inter-class confusion. To address these issues, a confusion-aware few-shot attention and scaling network (ConFAS-Net) is proposed. The method introduces a multi-scale channel attention module (MS-CA) to enhance adaptive extraction of multi-scale features, designs a confusion-aware loss optimization module (CACL) to guide discriminative feature learning using inter-class confusion information, and employs a class-adaptive decision adjustment module (CADA) to dynamically adjust classification boundaries for few-shot distribution characteristics. Extensive experiments on the standard MSTAR dataset demonstrated that ConFAS-Net achieved recognition accuracies of 73.25%, 87.43%, 94.97%, and 96.87% under 5-, 10-, 15-, and 30-shot settings, respectively. To rigorously substantiate the generalization capability and robustness of the proposed model across different data domains, additional validation was conducted on the public SAMPLE dataset, where ConFAS-Net consistently achieved state-of-the-art performance across all K-shot settings. Ablation studies and visualization analyses further validated the effectiveness of each proposed module. Comparisons with state-of-the-art methods demonstrate that the proposed method maintains high recognition accuracy while retaining a lightweight architecture of only 2.32 M parameters, providing an effective solution for SAR target recognition in resource-constrained environments. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101482 |
