Multimodal Remote Sensing Image Registration: A Comprehensive Review, Challenges and Prospects

2026-10-07

AI Summary

This study addresses the persistent challenge of registering multimodal remote sensing images complicated by radiometric and geometric discrepancies. It presents a systematic review of mainstream registration approaches, encompassing area-based, feature-based, and deep learning-based methods. By establishing a comprehensive taxonomy across the entire registration pipeline and conducting comparative evaluations on publicly available datasets, this work provides an in-depth analysis of the technical advantages and limitations inherent to each paradigm. Furthermore, it identifies the core bottlenecks currently hindering high-precision registration and outlines prospective directions for future research. Ultimately, this paper offers a holistic reference framework for the field, equipping researchers with a solid theoretical foundation and critical technical insights to advance multimodal remote sensing image registration.

Abstract

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.

Problem

• Multimodal Remote Sensing
• Image Registration
• Radiometric Variation
• Geometric Variation
• High-precision Registration

Innovation

• Multimodal Remote Sensing
• Image Registration
• Deep Learning
• Feature-based Methods
• Comprehensive Review