Harnessing conditional generative adversarial networks for SAR-to-optical image translation via auxiliary geospatial landscape pattern-augmentation
Hongbo Liang, Xuezhi Yang, Xiangyu Yang, Xiangyu Yang, Xiangyu Yang, Xin Jing · ISPRS Journal of Photogrammetry and Remote Sensing · 2026
Synthetic aperture radar (SAR) enables all-weather, all-day Earth observation, yet speckle noise and geometric distortions from complex electromagnetic scattering imaging severely limit its visual interpretability. SAR-to-optical image translation (S2OIT) has emerged to mitigate these challenges, but remains hindered by the data heterogeneity and spectral discrepancies between SAR and optical domains, where integrating auxiliary knowledge offers a viable remedy. What is more, previous studies rely on a pixel-wise constrained adversarial learning paradigm with limited mining of geospatial landscape information are prone to generating low-fidelity images. To tackle these issues, we propose AGPA-CGAN, a conditional generative adversarial network (CGAN) framework with auxiliary geospatial landscape pattern-augmentation for high-quality S2OIT. AGPA-CGAN progressively narrows the gap between translated and reference images by integrating ample SAR prior properties and geospatial structural information from scenario image pairs into the S2OIT process. Specifically, to fully exploit the tremendous priors of SAR images, we design an auxiliary pseudo-scattering pattern integration (APSPI) module to extract hierarchical subspace frequency conditional representations, thereby aiding AGPA-CGAN in capturing more descriptive cues for S2OIT. In particular, we introduce an unsupervised subspace embedding clustering (SEC) algorithm based on subspace frequency analysis (SSFA) within APSPI to derive statistical pseudo-scattering behavior maps against SAR feature spectrums. Furthermore, to stabilize the integration of SAR priors, we propose a geospatial landscape domain alignment (Geo-LDA) module that applies multi-perspective consistency regularization to align structural correspondences between SAR and optical features. Extensive experiments on three challenging benchmarks demonstrate that AGPA-CGAN surpasses state-of-the-art (SOTA) methods in both translation fidelity and structural realism.