PLISA: An Optical–SAR Remote Sensing Image Registration Method Based on Pseudo-Label Learning and Interactive Spatial Attention
Yixuan Zhang, Ruiqi Liu, Zeyu Zhang, Limin Shi, Lubin Weng, Lei Hu · Remote Sensing · 2025
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.