Parameter Retrieval of Attributed Scattering Center in Sar Image Using Reinforcement Learning

High-resolution SAR images can be modeled as the superposition of attributed scattering centers (ASC). Extracting ASC features from SAR images is considered to be a high-dimensional, nonlinear, and non-convex model parameter optimization problem. In early works, ASC parameter estimation is solved by performing scattering center segmentation from image domain SAR data with an iterative optimization algorithm, or performing Bayesian learning and dictionary-based learning from frequency domain SAR data. However, almost all these parameter estimation methods lack an efficient parameter update strategy, and the large parameter space brings great algorithm complexity. In this paper, it is proposed to extract ASCs by reinforcement learning by modeling the repetitive iterative process of parameter optimization as the interaction process between the agent and the environment, to improve the parameter update efficiency in the inference stage.