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| Citation: | JIA Hecheng, PU Xinyang, WANG Yanni, et al. Multi-view sample augumentation for SAR based on differentiable SAR renderer[J]. Journal of Radars, 2024, 13(2): 457–470. doi: 10.12000/JR24011
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Multi-view Sample Augumentation for SAR Based onDifferentiable SAR Renderer
doi: 10.12000/JR24011
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Abstract
Synthetic Aperture Radar (SAR) is extensively utilized in civilian and military domains due to its all-weather, all-time monitoring capabilities. In recent years, deep learning has been widely employed to automatically interpret SAR images. However, due to the constraints of satellite orbit and incident angle, SAR target samples face the issue of incomplete view coverage, which poses challenges for learning-based SAR target detection and recognition algorithms. This paper proposes a method for generating multi-view samples of SAR targets by integrating differentiable rendering, combining inverse Three-Dimensional (3D) reconstruction, and forward rendering techniques. By designing a Convolutional Neural Network (CNN), the proposed method inversely infers the 3D representation of targets from limited views of SAR target images and then utilizes a Differentiable SAR Renderer (DSR) to render new samples from more views, achieving sample interpolation in the view dimension. Moreover, the training process of the proposed method constructs the objective function using DSR, eliminating the need for 3D ground-truth supervision. According to experimental results on simulated data, this method can effectively increase the number of multi-view SAR target images and improve the recognition rate of typical SAR targets under few-shot conditions. -
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References
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- Figure 1. Analysis of insufficient view coverage for SAR targets
- Figure 2. Overall framework of multi-view sample augumentation for SAR
- Figure 3. Preprocessing of target and shadow regions
- Figure 4. Structure of the target reconstruction network
- Figure 5. Illustration of illumination map and shadow map generation
- Figure 6. Visualization of target reconstruction results
- Figure 7. Visualization of SAR vehicle target generation results
- Figure 8. Gamma distribution fitting of pixel values in target and background regions
- Figure 9. Evaluation of target reconstruction results with different numbers of views
- Figure 10. Illustration of target and shadow area segmentation for T72 measured samples
- Figure 11. Visualization of target 3D models reconstructed based on measured data
- Figure 12. Target and shadow areas in generated images
- Figure 13. Illustration of morphological processing in shadow areas
- Figure 14. Experimental results under different shadow segmentation accuracies
- Figure 15. Visualization of intermediate 3D models for each category
- Figure 16. Visualization comparison of generated samples and ground truth for each category
- Figure 17. Confusion matrices of recognition results before and after augmentation