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Learning Terrain Scattering Models From Massive Multisource Earth Observation Data
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AI
DOI：10.1109/TGRS.2025.3560749.png)
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Abstract
En 中文
This study presents a novel method for learning terrain scattering model parameters by leveraging massive multisource Earth observation (EO) 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, multiangle 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 (SP) maps while preserving texture features, with simulated multiangle SAR data based on these maps showing good radiometric consistency with measured data. This work holds considerable application potential, and integrating it with other multisource data and neural networks will yield more valuable outcomes in the future.
Keywords:
Scattering
Data models
Accuracy
Synthetic aperture radar
Data mining
Numerical models
Estimation
Earth
Surface waves
Surface roughness
Multisource Earth observation (EO)
parameter learning
synthetic aperture radar (SAR) data simulation
terrain scattering model
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W