$\mathbfΦ$-GAN: Physics-Inspired GAN for Generating SAR Images Under Limited Data
This addresses the challenge of generating realistic SAR images for remote sensing applications where data is scarce, though it is incremental as it builds on existing GAN methods with domain-specific adaptations.
The paper tackles the problem of generating synthetic aperture radar (SAR) images under limited data by proposing Φ-GAN, a physics-inspired GAN that incorporates an ideal point scattering center model and physical consistency losses, achieving state-of-the-art performance on three SAR datasets in data-scarce scenarios.
Approaches for improving generative adversarial networks (GANs) training under a few samples have been explored for natural images. However, these methods have limited effectiveness for synthetic aperture radar (SAR) images, as they do not account for the unique electromagnetic scattering properties of SAR. To remedy this, we propose a physics-inspired regularization method dubbed $Φ$-GAN, which incorporates the ideal point scattering center (PSC) model of SAR with two physical consistency losses. The PSC model approximates SAR targets using physical parameters, ensuring that $Φ$-GAN generates SAR images consistent with real physical properties while preventing discriminator overfitting by focusing on PSC-based decision cues. To embed the PSC model into GANs for end-to-end training, we introduce a physics-inspired neural module capable of estimating the physical parameters of SAR targets efficiently. This module retains the interpretability of the physical model and can be trained with limited data. We propose two physical loss functions: one for the generator, guiding it to produce SAR images with physical parameters consistent with real ones, and one for the discriminator, enhancing its robustness by basing decisions on PSC attributes. We evaluate $Φ$-GAN across several conditional GAN (cGAN) models, demonstrating state-of-the-art performance in data-scarce scenarios on three SAR image datasets.