PLISA: An Optical–SAR Remote Sensing Image Registration Method Based on Pseudo-Label Learning and Interactive Spatial Attention

Highlights: What are the main findings? PLISA is a self-supervised method for optical–SAR registration. It tackles key challenges by introducing a CIA module for feature alignment and a pseudo-label learning strategy, which generates supervision signals. Extensive experiments on multiple datasets demonstrate that PLISA surpasses state-of-the-art methods in accuracy and robustness under challenging conditions like rotation, scale changes, and noise. It also exhibits strong generalization across varying resolutions and cloud cover. What are the implications of the main finding? This study shows that self-supervised pseudo-labeling can address the scarcity of annotated keypoints in optical–SAR registration. PLISA maintains high accuracy with high computational efficiency, serving as a reliable tool for real-world remote sensing image registration. Multimodal remote sensing image registration faces severe challenges due to geometric and radiometric differences, particularly between optical and synthetic aperture radar (SAR) images. These inherent disparities make extracting highly repeatable cross-modal feature points difficult. Current methods typically rely on image intensity extreme responses or network regression without keypoint supervision for feature point detection. Moreover, they not only lack explicit keypoint annotations as supervision signals but also fail to establish a clear and consistent definition of what constitutes a reliable feature point in cross-modal scenarios. To overcome this limitation, we propose PLISA—a novel heterogeneous image registration method. PLISA integrates two core components: an automated pseudo-labeling module (APLM) and a pseudo-twin interaction network (PTIF). The APLM introduces an innovative labeling strategy that explicitly defines keypoints as corner points, thereby generating consistent pseudo-labels for dual-modality images and effectively mitigating the instability caused by the absence of supervised keypoint annotations. These pseudo-labels subsequently train the PTIF, which adopts a pseudo-twin architecture incorporating a cross-modal interactive attention (CIA) module to effectively reconcile cross-modal commonalities and distinctive characteristics. Evaluations on the SEN1-2 dataset and OSdataset demonstrate PLISA’s state-of-the-art cross-modal feature point repeatability while maintaining robust registration accuracy across a range of challenging conditions, including rotations, scale variations, and SAR-specific speckle noise.