TFCRNet: Dual-Discriminator SAR-to-Optical Translation and Region-Gated Cross-Attention Fusion for Thick-Cloud Removal

Abstract

Thick-cloud contamination severely limits the usability of optical remote sensing imagery because cloud-covered regions may suffer from complete loss of surface information. Synthetic aperture radar (SAR) imagery provides complementary structural cues due to its cloud-penetrating capability, but the substantial cross-modal discrepancy between SAR and optical images makes high-fidelity SAR–optical fusion challenging. Existing methods usually either directly fuse heterogeneous SAR and optical features or use SAR-to-optical translation with insufficient spectral and structural constraints, which may lead to spectral distortion, structural artifacts, or degradation of cloud-free regions. To address these issues, we propose TFCRNet, a two-stage translation-and-fusion network for SAR–optical thick-cloud removal. In the translation stage, a Multi-Scale Feature Fusion Generator (MSFFG) transforms SAR imagery into optical-like images, while a Spectral Discriminator (SpeD) and a Structural Discriminator (StrD) separately constrain spectral fidelity and structural integrity. In the fusion stage, a Region-Gated Cross-Attention Fusion (RGCAF) module performs cloud-aware feature interaction between the translated optical image and the cloudy optical image. Using an externally supplied cloud mask, RGCAF emphasizes translated SAR-derived cues in cloud-covered regions while retaining reliable optical information in cloud-free regions. TFCRNet therefore requires a cloud mask during inference. Experiments on the SEN12MS-CR and SMILE-CR datasets show that TFCRNet achieves the best overall performance among the baseline methods reproduced under the unified experimental protocol adopted in this study. TFCRNet obtains 33.01/30.53 dB PSNR and 0.91/0.88 SSIM on SEN12MS-CR and SMILE-CR, respectively. These controlled results should be distinguished from literature-reported values obtained under different experimental settings, several of which are higher on selected metrics. Fine-grained ablations demonstrate that SpeD and StrD provide differentiated spectral and structural supervision, while RGCAF improves multimodal reconstruction through cloud-mask-guided regional information routing rather than spatially uniform feature fusion.