Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review

被引：8

作者：

Saidi, Souad ^{[1
]}

Idbraim, Soufiane ^{[1
]}

Karmoude, Younes ^{[1
]}

Masse, Antoine ^{[2
]}

Arbelo, Manuel ^{[3
]}

机构：

[1] Ibn Zohr Univ, Fac Sci Agadir, IRF SIC Image Reconnaissance Formes Syst Intellige, Agadir 80000, Morocco

[2] Inst Geog Natl France Int, 7 rue Biscornet, F-75012 Paris, France

[3] Univ La Laguna, Dept Fis, Tenerife 38206, Spain

来源：

REMOTE SENSING | 2024年 / 16卷 / 20期

关键词：

change detection; deep learning; remote sensing images; data fusion; multi-source; multi-sensor; multi-modal; UNSUPERVISED CHANGE DETECTION; CONVOLUTIONAL NEURAL-NETWORK; LAND-COVER CLASSIFICATION; BUILDING CHANGE DETECTION; MULTISOURCE; FEATURES;

D O I：

10.3390/rs16203852

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Remote sensing images provide a valuable way to observe the Earth's surface and identify objects from a satellite or airborne perspective. Researchers can gain a more comprehensive understanding of the Earth's surface by using a variety of heterogeneous data sources, including multispectral, hyperspectral, radar, and multitemporal imagery. This abundance of different information over a specified area offers an opportunity to significantly improve change detection tasks by merging or fusing these sources. This review explores the application of deep learning for change detection in remote sensing imagery, encompassing both homogeneous and heterogeneous scenes. It delves into publicly available datasets specifically designed for this task, analyzes selected deep learning models employed for change detection, and explores current challenges and trends in the field, concluding with a look towards potential future developments.

引用

页数：32

共 198 条

[1] Evaluation of Deep Learning Techniques for Deforestation Detection in the Brazilian Amazon and Cerrado Biomes From Remote Sensing Imagery [J].