Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 24; p. 5128
• Main Authors: Zhang, Xinyue, Leng, Chengcai, Hong, Yameng, Pei, Zhao, Cheng, Irene, Basu, Anup
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021
• Subjects: Accuracy; Algorithms; area-based methods; Deep learning; deep-learning based methods; feature-based methods; Image registration; Machine learning; Methods; MMRS image registration; Photogrammetry; Radiation; Random variables; Registration; Remote sensing; Sensors; Similarity measures
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13245128

With rapid advancements in remote sensing image registration algorithms, comprehensive imaging applications are no longer limited to single-modal remote sensing images. Instead, multi-modal remote sensing (MMRS) image registration has become a research focus in recent years. However, considering multi-source, multi-temporal, and multi-spectrum input introduces significant nonlinear radiation differences in MMRS images for which researchers need to develop novel solutions. At present, comprehensive reviews and analyses of MMRS image registration methods are inadequate in related fields. Thus, this paper introduces three theoretical frameworks: namely, area-based, feature-based and deep learning-based methods. We present a brief review of traditional methods and focus on more advanced methods for MMRS image registration proposed in recent years. Our review or comprehensive analysis is intended to provide researchers in related fields with advanced understanding to achieve further breakthroughs and innovations.