Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

被引:35
|
作者
Zhang, Xinyue [1 ]
Leng, Chengcai [1 ]
Hong, Yameng [1 ]
Pei, Zhao [2 ]
Cheng, Irene [3 ]
Basu, Anup [3 ]
机构
[1] Northwest Univ, Sch Math, Xian 710127, Peoples R China
[2] Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China
[3] Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2E8, Canada
基金
中国国家自然科学基金; 加拿大自然科学与工程研究理事会;
关键词
MMRS image registration; area-based methods; feature-based methods; deep-learning based methods; SELF-SIMILARITY DESCRIPTOR; MUTUAL INFORMATION; AUTOMATIC REGISTRATION; PHASE CORRELATION; SAR; DEEP; SCALE; OPTIMIZATION; FRAMEWORK; HISTOGRAM;
D O I
10.3390/rs13245128
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:31
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