Volume 13 Issue 2
Apr.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(2): 457-470
Zhao Junxiang, Liang Xingdong, Li Yanlei. Change Detection in SAR CCD Based on the Likelihood Change Statistics[J]. Journal of Radars, 2017, 6(2): 186-194. doi: 10.12000/JR16065
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
shu

Multi-view Sample Augumentation for SAR Based onDifferentiable SAR Renderer

DOI: 10.12000/JR24011

  • Key Lab for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China

Funds:  The National Natural Science Foundation of China (61991422)
More Information
  • Corresponding author: XU Feng, fengxu@fudan.edu.cn
  • Received Date: 2024-01-16
  • Rev Recd Date: 2024-03-21
    • Available Online: 2024-03-25
  • Publish Date: 2024-03-28
    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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