Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR Images
Synthetic Aperture Radar (SAR) image target recognition technology based on deep learning has matured. However, challenges remain due to scattering phenomenon and noise interference that cause significant intraclass variability in imaging results. Invariant features, which represent the essential at...
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| Format: | Article |
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China Science Publishing & Media Ltd. (CSPM)
2025-04-01
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| Series: | Leida xuebao |
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| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR24125 |
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| author | Jingyi CAO Yang ZHANG Ya’nan YOU Yamin WANG Feng YANG Weijia REN Jun LIU |
| author_facet | Jingyi CAO Yang ZHANG Ya’nan YOU Yamin WANG Feng YANG Weijia REN Jun LIU |
| author_sort | Jingyi CAO |
| collection | DOAJ |
| description | Synthetic Aperture Radar (SAR) image target recognition technology based on deep learning has matured. However, challenges remain due to scattering phenomenon and noise interference that cause significant intraclass variability in imaging results. Invariant features, which represent the essential attributes of a specific target class with consistent expressions, are crucial for high-precision recognition. We define these invariant features from the entity, its surrounding environment, and their combined context as the target’s essential features. Guided by multilevel essential feature modeling theory, we propose a SAR image target recognition method based on graph networks and invariant feature perception. This method employs a dual-branch network to process multiview SAR images simultaneously using a rotation-learnable unit to adaptively align dual-branch features and reinforce invariant features with rotational immunity by minimizing intraclass feature differences. Specifically, to support essential feature extraction in each branch, we design a feature-guided graph feature perception module based on multilevel essential feature modeling. This module uses salient points for target feature analysis and comprises a target ontology feature enhancement unit, an environment feature sampling unit, and a context-based adaptive fusion update unit. Outputs are analyzed with a graph neural network and constructed into a topological representation of essential features, resulting in a target category vector. The t-Distributed Stochastic Neighbor Embedding (t-SNE) method is used to qualitatively evaluate the algorithm’s classification ability, while metrics like accuracy, recall, and F1 score are used to quantitatively analyze key units and overall network performance. Additionally, class activation map visualization methods are employed to validate the extraction and analysis of invariant features at different stages and branches. The proposed method achieves recognition accuracies of 98.56% on the MSTAR dataset, 94.11% on SAR-ACD dataset, and 86.20% on OpenSARShip dataset, demonstrating its effectiveness in extracting essential target features. |
| format | Article |
| id | doaj-art-55a6f64b154c4f949991d7ec2d9cbf86 |
| institution | DOAJ |
| issn | 2095-283X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj-art-55a6f64b154c4f949991d7ec2d9cbf862025-08-20T02:53:33ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2025-04-0114236638810.12000/JR24125R24125Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR ImagesJingyi CAO0Yang ZHANG1Ya’nan YOU2Yamin WANG3Feng YANG4Weijia REN5Jun LIU6School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaChangsha Tianyi Space Science and Technology Research Institute Co., Ltd., Changsha 410221, ChinaChangsha Tianyi Space Science and Technology Research Institute Co., Ltd., Changsha 410221, ChinaSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSynthetic Aperture Radar (SAR) image target recognition technology based on deep learning has matured. However, challenges remain due to scattering phenomenon and noise interference that cause significant intraclass variability in imaging results. Invariant features, which represent the essential attributes of a specific target class with consistent expressions, are crucial for high-precision recognition. We define these invariant features from the entity, its surrounding environment, and their combined context as the target’s essential features. Guided by multilevel essential feature modeling theory, we propose a SAR image target recognition method based on graph networks and invariant feature perception. This method employs a dual-branch network to process multiview SAR images simultaneously using a rotation-learnable unit to adaptively align dual-branch features and reinforce invariant features with rotational immunity by minimizing intraclass feature differences. Specifically, to support essential feature extraction in each branch, we design a feature-guided graph feature perception module based on multilevel essential feature modeling. This module uses salient points for target feature analysis and comprises a target ontology feature enhancement unit, an environment feature sampling unit, and a context-based adaptive fusion update unit. Outputs are analyzed with a graph neural network and constructed into a topological representation of essential features, resulting in a target category vector. The t-Distributed Stochastic Neighbor Embedding (t-SNE) method is used to qualitatively evaluate the algorithm’s classification ability, while metrics like accuracy, recall, and F1 score are used to quantitatively analyze key units and overall network performance. Additionally, class activation map visualization methods are employed to validate the extraction and analysis of invariant features at different stages and branches. The proposed method achieves recognition accuracies of 98.56% on the MSTAR dataset, 94.11% on SAR-ACD dataset, and 86.20% on OpenSARShip dataset, demonstrating its effectiveness in extracting essential target features.https://radars.ac.cn/cn/article/doi/10.12000/JR24125synthetic aperture radar (sar)target recognitioninvariant feature extractionessential featuredeep learning |
| spellingShingle | Jingyi CAO Yang ZHANG Ya’nan YOU Yamin WANG Feng YANG Weijia REN Jun LIU Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR Images Leida xuebao synthetic aperture radar (sar) target recognition invariant feature extraction essential feature deep learning |
| title | Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR Images |
| title_full | Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR Images |
| title_fullStr | Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR Images |
| title_full_unstemmed | Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR Images |
| title_short | Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR Images |
| title_sort | target recognition method based on graph structure perception of invariant features for sar images |
| topic | synthetic aperture radar (sar) target recognition invariant feature extraction essential feature deep learning |
| url | https://radars.ac.cn/cn/article/doi/10.12000/JR24125 |
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