Graph neural network based SAR automatic target recognition with human-in-the-loop

Abstract

Synthetic Aperture Radar (SAR) automatic target recognition (ATR) is a key technique for SAR image analysis in military activities. Accurate SAR ATR can promote command and decision-making. In this work, we propose a novel SAR ATR framework with human-in-the-loop. The framework consists of a Reinforcement Learning (RL) Agent, which is followed by a GNN-based classifier. The RL Agent is capable of learning from human feedback to identify the region of target (RoT) in the SAR image. The RoT is then used to construct the input graph for the GNN classifier to perform target classification. By learning from human feedback, the RL Agent can focus on the RoT and filter out irrelevant and distracting signals in the input SAR images. We evaluate the proposed framework on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The results show that incorporating human feedback can improve classification accuracy. By visualizing the results, we observe that the RL Agent can effectively reduce irrelevant SAR signals in the input SAR images after learning from human feedback.