Soft Segmented Randomization: Enhancing Domain Generalization in SAR-ATR for Synthetic-to-Measured
This work addresses domain generalization for synthetic aperture radar automatic target recognition, offering a solution for scenarios with limited real data, though it appears incremental as it builds on existing methods to reduce domain gaps.
The study tackled the problem of domain discrepancy between synthetic and real synthetic aperture radar data for automatic target recognition by introducing a soft segmented randomization framework, which significantly enhanced model performance on measured data.
Synthetic aperture radar technology is crucial for high-resolution imaging under various conditions; however, the acquisition of real-world synthetic aperture radar data for deep learning-based automatic target recognition remains challenging due to high costs and data availability issues. To overcome these challenges, synthetic data generated through simulations have been employed, although discrepancies between synthetic and real data can degrade model performance. In this study, we introduce a novel framework, soft segmented randomization, designed to reduce domain discrepancy and improve the generalize ability of synthetic aperture radar automatic target recognition models. The soft segmented randomization framework applies a Gaussian mixture model to segment target and clutter regions softly, introducing randomized variations that align the synthetic data's statistical properties more closely with those of real-world data. Experimental results demonstrate that the proposed soft segmented randomization framework significantly enhances model performance on measured synthetic aperture radar data, making it a promising approach for robust automatic target recognition in scenarios with limited or no access to measured data.