Radiation, Rotation and Scale Invariant Feature Descriptor for Multimodal Image Matching

2026-09-06

AI Summary

本文提出一种辐射、旋转和尺度不变（RRSI）特征描述符，通过双头区域采样模块和深度特征空间联合编码解决多模态图像匹配中的几何失真和非线性辐射差异问题。

Abstract

Multimodal image matching is a fundamental task for multi-source information fusion. However, geometric distortions and nonlinear radiometric differences (NRD) severely limit performance, especially under radiometric, rotation, and scale variations. To address this issue, we propose a radiation, rotation, and scale invariant (RRSI) feature descriptor. First, a dual-head regional sampling (DHRS) module simultaneously performs Cartesian and Log-Polar sampling on keypoint neighborhoods, retaining spatial structural properties while enhancing robustness to rotation and scale variations. We then jointly encode geometric and radiometric relations between multimodal images in a unified deep feature space, enabling feature encoding, interaction, and fusion across intra-modal, dual-head sampled, and inter-modal regions. Furthermore, we introduce a bidirectional cross-modal generative reconstruction constraint during training. By decoding implicit features into structural patches of the counterpart modality, this mechanism anchors modality-invariant geometric topologies without additional inference overhead. Experiments on optical-infrared and optical-SAR datasets demonstrate highly competitive matching performance and strong robustness to rotation and scale variations. RRSI supports the full rotation range from 0 to 360 degrees and scale factors up to four. Its generalization ability is further validated on multimodal images from computer vision, remote sensing, and medical imaging. The implementation will be made publicly available at https://github.com/yeyuanxin110/RRSI .

Problem

• multimodal image matching
• geometric distortions
• nonlinear radiometric differences
• radiation variations
• rotation and scale variations

Innovation

• Radiation, Rotation and Scale Invariant (RRSI)
• Dual-head Regional Sampling (DHRS)
• Bidirectional Cross-modal Generative Reconstruction