Abstract
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.
Highlights
Image registration is the process of geometrical alignment and matching of two or more images of the same scene, acquired from different sensors, with different views and at different times [1]
We review the general methods of multimodal remote sensing (MMRS) image registration, especially classification according to the registration method category, and introduce recently popular learning-based methods, so that readers can learn about cutting-edge methods in the field at a glance
A radiation insensitive image registration method based on phase congruency (PC) and a maximum index image (MIM), which was called radiation variation insensitive feature transform (RIFT)
Summary
Image registration is the process of geometrical alignment and matching of two or more images of the same scene, acquired from different sensors, with different views and at different times [1]. It is possible to obtain multi-source remote sensing image data with the rapid development of aerospace technology and remote sensing. Comprehensive utilization of multi-source remote sensing data has been widely used to realize the uniqueness and complementarity of different remote sensing images, in order to acquire images containing more information. The registration of MMRS images still has difficulties in applications due to significant geometric distortion and nonlinear intensity differences between these images. Different sensors capture optical and synthetic aperture radar (SAR) images, and different imaging mechanisms produce distinct characteristics of an area. When encountering emergencies, such as weather disasters, only SAR images are useful since they can work during both day and night and see-through cloud and fog to capture images
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