CSIFT: A Structural Feature Encoding Framework for Cross-Modal Image Registration

Multimodal sensor collaborative detection serves as an indispensable complementary technology in Earth observation. However, significant cross-modal discrepancies in multimodal images severely limit the operational efficiency and alignment accuracy of traditional registration methods. Feature-based multimodal image registration techniques struggle to maintain stable performance across different modalities due to inherent radiometric differences. To address this limitation, this article proposes a cross-modal structural information feature transform (CSIFT) framework by optimizing intermediate modality construction. The method employs the oriented FAST and rotated BRIEF detector to extract keypoints and construct circular Gaussian-weighted oriented gradient features (CGOGFs) through a Gaussian-weighted directional gradient allocation mechanism applied to neighborhood information. The gradient magnitude distribution based on annular distance eliminates truncation effects caused by linear interpolation, while principal direction-aligned blocks ensure rotational consistency. By transforming cross-modal registration into a homogeneous feature matching problem through structural feature encoding, experimental results demonstrate that CGOGF significantly enhances cross-modal performance while preserving information integrity compared to traditional methods. Extensive evaluations on two public datasets show that CSIFT achieves the highest success rates (97.58% and 97.19% ) across various multimodal datasets, outperforming multiple traditional registration approaches.