Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery
Radar cross-section (RCS) modeling is foundational to advancing the utility and sensitivity of spaceborne radar systems. This study introduces a deep sigma-point process (DSPP) model for predicting RCS in synthetic aperture radar (SAR) imagery using a RADARSAT-2 dataset containing 208,191 verified ships. The DSPP model not only strives for predictive accuracy but also characterizes the uncertainty inherent in the intricate relationships among radar signals, ship parameters, and environmental conditions. Unlike traditional approaches that rely on deterministic equations with static parameters, the DSPP uses a hierarchical Gaussian process framework with Bayesian inference to capture variab...
| Authors | Khalid El-Darymli, Christoph H. Gierull, Katerina Biron et al. |
|---|---|
| Primary category | eess.SP |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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