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

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, hypersp...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-10, Vol.16 (20), p.3852
Main Authors: Saidi, Souad, Idbraim, Soufiane, Karmoude, Younes, Masse, Antoine, Arbelo, Manuel
Format: Article
Language:English
Subjects:
Accuracy
Change detection
Classification
data fusion
Decision making
Deep learning
Earth surface
Environmental monitoring
Keywords
Literature reviews
multi-sensor
multi-source
Natural resources
Neural networks
Observational learning
Radar detection
Radar imaging
Radar systems
Remote sensing
remote sensing images
Remote sensing systems
Satellite imagery
Satellite observation
Satellites
Search strategies
Sensors
Statistical analysis
Trends
Urban planning
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16203852