Multi-view Sample Augumentation for SAR Based onDifferentiable SAR Renderer

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.

Keywords

• synthetic aperture radar (sar)
• differentiable sar renderer (dsr)
• convolutional neural network (cnn)
• 3d reconstruction
• multi-view sample generation