Learning Terrain Scattering Models From Massive Multisource Earth Observation Data

This study presents a novel method for learning terrain scattering model parameters by leveraging massive multi-source Earth observation data, aiming to achieve realistic Synthetic Aperture Radar (SAR) data simulation. By integrating Gaofen-3 and Sentinel-1 SAR data with auxiliary datasets, the scattering characteristics of various terrains were extracted and analyzed with respect to angle, season, and resolution. For forward modeling, the scattering models were compared to identify suitable models and parameters. To address the challenge of multiple solutions in parameter learning, multi-angle scattering characteristics were employed for initial value estimation, supported by a probability density-based loss function. During parameter learning, targeted learning rates were set based on the gradients of the scattering models with respect to the parameters. Extensive evaluations demonstrate that the proposed method reliably estimates scene parameter maps while preserving texture features, with simulated multi-angle SAR data based on these maps showing good radiometric consistency with measured data. This work holds considerable application potential, and integrating it with other multi-source data and neural networks will yield more valuable outcomes in the future.