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The integration of the frequency-domain electromagnetic reconstruction algorithm with image-domain cropping optimization achieves an effective balance between reconstruction accuracy and computational efficiency. The integration of electromagnetic reconstruction and feature alignment effectively enhances model robustness and suppresses background clutter in SAR ATR under varying operating conditions.
Provides a trustworthy deep learning solution for SAR ATR by aligning electromagnetic reconstructions with image features, which helps mitigate overfitting to specific operating conditions. Provides evidence that utilizing target-related physical features significantly enhances the robustness, generalization and interpretability of deep learning-based SAR ATR. 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.
Details
Dictionaries;
Deep learning;
Algorithms;
Automatic target recognition;
Parameter sensitivity;
Image processing;
Matched pursuit;
Machine learning;
Data compression;
Transfer learning;
Alignment;
Physics;
Image reconstruction;
Fourier transforms;
Synthetic aperture radar;
Target recognition;
Neural networks;
Optimization;
Crops;
Clutter;
Robustness (mathematics);
Trustworthiness
1 Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai 200240, China; [email protected] (Y.G.); [email protected] (J.L.); [email protected] (Y.W.); [email protected] (W.Y.)
2 Center for Digital Innovation, Tongji University, Shanghai 200092, China; [email protected]