A Reinforcement Learning Framework for Scattering Feature Extraction and SAR Image Interpretation

With the rapid development and widespread deployment of radar technology, the interpretation of the vast amount of synthetic aperture radar (SAR) imagery obtained daily has emerged as a hot topic. The complexity of the electromagnetic scattering mechanisms contained within radar images makes SAR image interpretation a challenging task. Current methodologies for SAR image interpretation primarily involve feature extraction-based techniques, categorized into image-domain and frequency-domain algorithms. However, these methods are faced with issues, such as rough segmentation in images, high-computational complexity, and poor robustness, presenting significant challenges in the field. In this article, a novel framework for SAR image interpretation is proposed, leveraging reinforcement learning (RL) for the extraction of electromagnetic scattering features and the inversion of parameters. Within this framework, a nonsparse reward function, combined with curriculum learning, is introduced as the supervisory information. It enables more efficient policy updates through a structured two-stage training approach. In addition, an algorithm that integrates a four-neighbor breadth-first search (BFS) with the watershed segmentation process is proposed, aiming to enhance the accuracy of scattering center analysis in SAR imagery. The attribute scattering center model (ASCM) is utilized as a prototype for conducting algorithmic research and experimentation. Experimental results on both simulation data and measured data have indicated that the proposed method significantly improves efficiency while ensuring accuracy, demonstrating its capability to extract parameters from measured data in most scenarios.