Advances and Challenges in Multimodal Remote Sensing Image Registration

TL;DR

This paper reviews the progress, challenges, and future directions in multimodal remote sensing image registration, emphasizing its importance for integrating diverse sensor data for comprehensive Earth observation.

Contribution

The paper presents a summary of existing methods, analyzes their advantages and limitations, and discusses future challenges and prospects in multimodal remote sensing image registration.

Findings

Existing methods have limitations in accuracy and robustness.

Multimodal registration remains challenging due to sensor heterogeneity.

Future research should focus on adaptive and deep learning approaches.

Abstract

Over the past few decades, with the rapid development of global aerospace and aerial remote sensing technology, the types of sensors have evolved from the traditional monomodal sensors (e.g., optical sensors) to the new generation of multimodal sensors [e.g., multispectral, hyperspectral, light detection and ranging (LiDAR) and synthetic aperture radar (SAR) sensors]. These advanced devices can dynamically provide various and abundant multimodal remote sensing images with different spatial, temporal, and spectral resolutions according to different application requirements. Since then, it is of great scientific significance to carry out the research of multimodal remote sensing image registration, which is a crucial step for integrating the complementary information among multimodal data and making comprehensive observations and analysis of the Earths surface. In this work, we will present our own contributions to the field of multimodal image registration, summarize the advantages and limitations of existing multimodal image registration methods, and then discuss the remaining challenges and make a forward-looking prospect for the future development of the field.