Few-Shot SAR Target Recognition via Causal Inference and Deep Metric Learning
Deep learning, with large-scale annotated datasets, has demonstrated remarkable success in synthetic aperture radar automatic target recognition (SAR-ATR). However, the collecting of SAR images is expensive and complex, and manually labeling them requires expert knowledge. To overcome these limitati...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11080412/ |
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| author | Ke Wang Yuqian Mao Qi Qiao |
| author_facet | Ke Wang Yuqian Mao Qi Qiao |
| author_sort | Ke Wang |
| collection | DOAJ |
| description | Deep learning, with large-scale annotated datasets, has demonstrated remarkable success in synthetic aperture radar automatic target recognition (SAR-ATR). However, the collecting of SAR images is expensive and complex, and manually labeling them requires expert knowledge. To overcome these limitations, we propose a few-shot learning model capable of accurate recognition of novel targets with minimal training samples. Our model innovatively integrates causal inference with mutual centralized learning (MCL) to address few-shot SAR-ATR tasks. First, we establish a causal inference framework to identify and model the dependencies among target characteristics, imaging conditions, and category labels. This framework incorporates a novel causal intervention method based on multi-scale random convolution to eliminate spurious correlations caused by imaging variations, thereby enhancing feature stability. Second, we introduce an advanced MCL module to effectively evaluate feature similarity in few-shot settings. MCL breaks through the unidirectional matching paradigm adopted by conventional metric learning. Through its bidirectional feature interactions and dense feature accessibility mechanisms, MCL achieves more robust feature discrimination in few-shot learning tasks. Comprehensive experiments demonstrate that our model outperforms existing advanced few-shot SAR-ATR methods, achieving superior recognition accuracy while maintaining robustness in data-scarce scenarios. |
| format | Article |
| id | doaj-art-d9bac4a12ea640e1a9f8e28cade6e184 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d9bac4a12ea640e1a9f8e28cade6e1842025-08-20T02:45:42ZengIEEEIEEE Access2169-35362025-01-011312498812500210.1109/ACCESS.2025.358919211080412Few-Shot SAR Target Recognition via Causal Inference and Deep Metric LearningKe Wang0https://orcid.org/0000-0003-2982-4153Yuqian Mao1https://orcid.org/0000-0002-1615-4165Qi Qiao2https://orcid.org/0009-0000-2334-8956School of Computer and Communication, Jiangsu Vocational College of Electronics and Information, Huaian, ChinaThe Eighth Research Institute of China, China State Shipbuilding Corporation Ltd., Nanjing, ChinaSchool of Computer and Communication, Jiangsu Vocational College of Electronics and Information, Huaian, ChinaDeep learning, with large-scale annotated datasets, has demonstrated remarkable success in synthetic aperture radar automatic target recognition (SAR-ATR). However, the collecting of SAR images is expensive and complex, and manually labeling them requires expert knowledge. To overcome these limitations, we propose a few-shot learning model capable of accurate recognition of novel targets with minimal training samples. Our model innovatively integrates causal inference with mutual centralized learning (MCL) to address few-shot SAR-ATR tasks. First, we establish a causal inference framework to identify and model the dependencies among target characteristics, imaging conditions, and category labels. This framework incorporates a novel causal intervention method based on multi-scale random convolution to eliminate spurious correlations caused by imaging variations, thereby enhancing feature stability. Second, we introduce an advanced MCL module to effectively evaluate feature similarity in few-shot settings. MCL breaks through the unidirectional matching paradigm adopted by conventional metric learning. Through its bidirectional feature interactions and dense feature accessibility mechanisms, MCL achieves more robust feature discrimination in few-shot learning tasks. Comprehensive experiments demonstrate that our model outperforms existing advanced few-shot SAR-ATR methods, achieving superior recognition accuracy while maintaining robustness in data-scarce scenarios.https://ieeexplore.ieee.org/document/11080412/Synthetic aperture radar (SAR)target recognitiondeep metric learningcausal inference |
| spellingShingle | Ke Wang Yuqian Mao Qi Qiao Few-Shot SAR Target Recognition via Causal Inference and Deep Metric Learning IEEE Access Synthetic aperture radar (SAR) target recognition deep metric learning causal inference |
| title | Few-Shot SAR Target Recognition via Causal Inference and Deep Metric Learning |
| title_full | Few-Shot SAR Target Recognition via Causal Inference and Deep Metric Learning |
| title_fullStr | Few-Shot SAR Target Recognition via Causal Inference and Deep Metric Learning |
| title_full_unstemmed | Few-Shot SAR Target Recognition via Causal Inference and Deep Metric Learning |
| title_short | Few-Shot SAR Target Recognition via Causal Inference and Deep Metric Learning |
| title_sort | few shot sar target recognition via causal inference and deep metric learning |
| topic | Synthetic aperture radar (SAR) target recognition deep metric learning causal inference |
| url | https://ieeexplore.ieee.org/document/11080412/ |
| work_keys_str_mv | AT kewang fewshotsartargetrecognitionviacausalinferenceanddeepmetriclearning AT yuqianmao fewshotsartargetrecognitionviacausalinferenceanddeepmetriclearning AT qiqiao fewshotsartargetrecognitionviacausalinferenceanddeepmetriclearning |