Boosting SAR ATR Trustworthiness via ERFA: An Electromagnetic Reconstruction Feature Alignment Method
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| Argitaratua izan da: | Remote Sensing vol. 17, no. 23 (2025), p. 3855-3882 |
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MDPI AG
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| Sarrera elektronikoa: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17233855 |2 doi | |
| 035 | |a 3280962905 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Gao Yuze |u Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai 200240, China; gaoyuze@sjtu.edu.cn (Y.G.); jianyu.l.wl@sjtu.edu.cn (J.L.); wyiren2020@sjtu.edu.cn (Y.W.); wxyu@sjtu.edu.cn (W.Y.) | |
| 245 | 1 | |a Boosting SAR ATR Trustworthiness via ERFA: An Electromagnetic Reconstruction Feature Alignment Method | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a <sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>The integration of the frequency-domain electromagnetic reconstruction algorithm with image-domain cropping optimization achieves an effective balance between reconstruction accuracy and computational efficiency. <list-item> The integration of electromagnetic reconstruction and feature alignment effectively enhances model robustness and suppresses background clutter in SAR ATR under varying operating conditions. </list-item> What are the implications of the main findings? <list list-type="bullet"> <list-item> </list-item>Provides a trustworthy deep learning solution for SAR ATR by aligning electromagnetic reconstructions with image features, which helps mitigate overfitting to specific operating conditions. <list-item> Provides evidence that utilizing target-related physical features significantly enhances the robustness, generalization and interpretability of deep learning-based SAR ATR. </list-item> Deep learning-based synthetic aperture radar (SAR) automatic target recognition (ATR) methods exhibit a tendency to overfit specific operating conditions—such as radar parameters and background clutter—which frequently leads to high sensitivity against variations in these conditions. A novel electromagnetic reconstruction feature alignment (ERFA) method is proposed in this paper, which integrates electromagnetic reconstruction with feature alignment into a fully convolutional network, forming the ERFA-FVGGNet. The ERFA-FVGGNet comprises three modules: electromagnetic reconstruction using our proposed orthogonal matching pursuit with image-domain cropping-optimization (OMP-IC) algorithm for efficient, high-precision attributed scattering center (ASC) reconstruction and extraction; the designed FVGGNet combining transfer learning with a lightweight fully convolutional network to enhance feature extraction and generalization; and feature alignment employing a dual-loss to suppress background clutter while improving robustness and interpretability. Experimental results demonstrate that ERFA-FVGGNet boosts trustworthiness by enhancing robustness, generalization and interpretability. | |
| 653 | |a Feature extraction | ||
| 653 | |a Dictionaries | ||
| 653 | |a Deep learning | ||
| 653 | |a Algorithms | ||
| 653 | |a Automatic target recognition | ||
| 653 | |a Parameter sensitivity | ||
| 653 | |a Image processing | ||
| 653 | |a Matched pursuit | ||
| 653 | |a Machine learning | ||
| 653 | |a Data compression | ||
| 653 | |a Transfer learning | ||
| 653 | |a Alignment | ||
| 653 | |a Physics | ||
| 653 | |a Image reconstruction | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Synthetic aperture radar | ||
| 653 | |a Target recognition | ||
| 653 | |a Neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Crops | ||
| 653 | |a Clutter | ||
| 653 | |a Robustness (mathematics) | ||
| 653 | |a Trustworthiness | ||
| 700 | 1 | |a Li Dongying |u Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai 200240, China; gaoyuze@sjtu.edu.cn (Y.G.); jianyu.l.wl@sjtu.edu.cn (J.L.); wyiren2020@sjtu.edu.cn (Y.W.); wxyu@sjtu.edu.cn (W.Y.) | |
| 700 | 1 | |a Guo Weiwei |u Center for Digital Innovation, Tongji University, Shanghai 200092, China; weiweiguo@tongji.edu.cn | |
| 700 | 1 | |a Lin, Jianyu |u Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai 200240, China; gaoyuze@sjtu.edu.cn (Y.G.); jianyu.l.wl@sjtu.edu.cn (J.L.); wyiren2020@sjtu.edu.cn (Y.W.); wxyu@sjtu.edu.cn (W.Y.) | |
| 700 | 1 | |a Wang, Yiren |u Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai 200240, China; gaoyuze@sjtu.edu.cn (Y.G.); jianyu.l.wl@sjtu.edu.cn (J.L.); wyiren2020@sjtu.edu.cn (Y.W.); wxyu@sjtu.edu.cn (W.Y.) | |
| 700 | 1 | |a Yu, Wenxian |u Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai 200240, China; gaoyuze@sjtu.edu.cn (Y.G.); jianyu.l.wl@sjtu.edu.cn (J.L.); wyiren2020@sjtu.edu.cn (Y.W.); wxyu@sjtu.edu.cn (W.Y.) | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 23 (2025), p. 3855-3882 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3280962905/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3280962905/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3280962905/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |