Speckle-Based Residual Optronic Convolutional Neural Network for SAR Target Recognition in Scattering Imaging Scenarios

Scattering imaging is a pervasive scenario in many areas, especially challenging the performance of remote sensing and automatic target recognition (ATR). Recently, deep learning was utilized for synthetic aperture radar (SAR) ATR in scattering scenarios by extracting the feature of speckle patterns. However, huge computational costs and power consumption challenge its development. Here, we develop a speckle-based residual optronic convolutional neural network (S-ROPCNN) for SAR target recognition. Specifically, we model the light scattering scenarios and build the optical imaging system to produce the speckle patterns for network training. The S-ROPCNN performs SAR target recognition in optical platforms with the speed of light, low computational cost, and low energy consumption. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the feasibility of S-ROPCNN for SAR target recognition in scattering imaging scenarios.