发表机构
Southwest Jiaotong University; Kunming University of Science and Technology; Beijing Jiaotong University（西南交通大学; 昆明理工大学; 北京交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述多模态遥感图像配准方法，将其分为三类，总结相关数据集，指出当前瓶颈并展望趋势，为相关研究者提供参考。
AI 中文摘要
多模态遥感图像配准是遥感数据协同处理及下游应用（如图像融合、变化检测、目标识别）的关键前提。然而，多模态图像间常存在辐射、几何、尺度、视角、时间上的显著差异，这些差异由传感器几何结构、物理辐射机制、成像平台及环境干扰导致，对实现高精度、鲁棒配准构成严峻挑战。本文系统综述主流多模态遥感图像配准方法的进展，依据配准流程将现有方法分为基于区域、基于特征、基于深度学习三类，详细阐述每类方法的核心原理、代表性算法、优势与局限性；同时总结遥感领域公开可用的多模态图像数据集，分析其具体特性与适用场景；最后指出高精度配准研究的当前瓶颈，展望未来发展趋势，旨在为相关领域研究者提供全面参考与宝贵见解。
英文摘要
Multimodal remote sensing image registration is a crucial prerequisite for the collaborative processing and downstream application of remote sensing data, such as image fusion, change detection, and target recognition. However, significant variations in radiometry, geometry, scale, viewpoint, and time often exist between multimodal images. These differences, driven by varying sensor geometries, physical radiation mechanisms, imaging platforms, and environmental disturbances, pose severe challenges to achieving high-precision, robust registration. This paper systematically reviews the progress of mainstream multimodal remote sensing image registration methods. Based on their registration pipelines, existing approaches are categorized into three main types: region-based, feature-based, and deep learning-based methods. We detail the core principles, representative algorithms, advantages, and limitations of each category. Additionally, we summarize publicly available multimodal image datasets in the remote sensing domain, analyzing their specific characteristics and applicable scenarios. Finally, we highlight current bottlenecks in high-precision registration research and outline future development trends. This review aims to provide a comprehensive reference and valuable insights for researchers in related fields.
Comments12 figures, 8 tables, 135 references. Review article accepted for publication in Photogrammetric Engineering and Remote Sensing (ASPS), manuscript number PERS-26-00034
