Cross-Modal Feature Calibration and Fusion Network for Remote Sensing Optical-SAR Joint Object Detection under Cloud Occlusion

Optical-SAR fusion object detection has drawn great attention for its ability to combine the rich visual features of optical images with the all-weather, all-day imaging capability of SAR, enabling robust detection performance under adverse weather conditions. However, cloud occlusion causes severe degradation in optical images, while the weak texture of SAR images leads to missed detections, which poses significant challenges to accurate object detection. To address this issue, we propose a novel Cross-Modal Feature Calibration and Fusion Network (CFCFNet) for robust object detection under cloud occlusion. Specifically, we propose an R2U-Net-based Cloud Removal (R2U-CR) module to enable end-to-end cloud removal at a low computational cost. To enhance feature representations and mitigate cross-modal discrepancies, a Cross-Modal Feature Calibration (CMFC) module is introduced for channel-wise and spatial feature alignment. Finally, to fully exploit the complementary information across modalities, we design a Convolutional Attention-based Dynamic Fusion (CADF) module that adaptively integrates global context and local details to enhance the discriminative capability of fused features. Besides, we construct the OPTSAR-Cloud dataset, comprising five sets of 3,300 optical-SAR image pairs under varying cloud thickness, enabling evaluation under complex atmospheric environments. Extensive experiments on the OPTSAR-Cloud dataset demonstrate that CFCFNet achieves state-of-the-art performance, with an absolute gain of 2.7% in mAP50 over previous best approaches. Ablation studies under thick cloud conditions further show that CFCFNet improves detection accuracy by 5.6%, increasing mAP50 from 60.8% to 66.4%.