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] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194141007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: ConFAS-Net: Few-Shot SAR Target Recognition via Confusion-Aware Attention and Adaptive Decision Scaling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Xin%22">Zhao, Xin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xue%2C+Xiaorong%22">Xue, Xiaorong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xr_986@163.com</i><br /><searchLink fieldCode="AR" term="%22Tian%2C+Yishuo%22">Tian, Yishuo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Jingtong%22">Yang, Jingtong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Bingyan%22">Lu, Bingyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Wen%22">Zhang, Wen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Wancheng%22">Wang, Wancheng</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1482. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Automatic+target+recognition%22">Automatic target recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Synthetic+aperture+radar%22">Synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194141007 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18101482 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1482 Subjects: – SubjectFull: Automatic target recognition Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Cost functions Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Synthetic aperture radar Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: ConFAS-Net: Few-Shot SAR Target Recognition via Confusion-Aware Attention and Adaptive Decision Scaling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Xin – PersonEntity: Name: NameFull: Xue, Xiaorong – PersonEntity: Name: NameFull: Tian, Yishuo – PersonEntity: Name: NameFull: Yang, Jingtong – PersonEntity: Name: NameFull: Lu, Bingyan – PersonEntity: Name: NameFull: Zhang, Wen – PersonEntity: Name: NameFull: Wang, Wancheng IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 10 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
