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A Review of Deep-Learning Methods for Change Detection in Multispectral Remote Sensing Images
by
Parelius, Eleonora Jonasova
in
Algebra
/ Artificial intelligence
/ automation
/ Cartography
/ Change detection
/ Classification
/ Comparative analysis
/ data collection
/ Deep learning
/ Image processing
/ Machine learning
/ Methods
/ multispectral imaging
/ Multispectral photography
/ Neural networks
/ optical imaging
/ Remote sensing
/ Semantics
/ Sensors
/ Technology application
/ Urban planning
2023
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A Review of Deep-Learning Methods for Change Detection in Multispectral Remote Sensing Images
by
Parelius, Eleonora Jonasova
in
Algebra
/ Artificial intelligence
/ automation
/ Cartography
/ Change detection
/ Classification
/ Comparative analysis
/ data collection
/ Deep learning
/ Image processing
/ Machine learning
/ Methods
/ multispectral imaging
/ Multispectral photography
/ Neural networks
/ optical imaging
/ Remote sensing
/ Semantics
/ Sensors
/ Technology application
/ Urban planning
2023
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Do you wish to request the book?
A Review of Deep-Learning Methods for Change Detection in Multispectral Remote Sensing Images
by
Parelius, Eleonora Jonasova
in
Algebra
/ Artificial intelligence
/ automation
/ Cartography
/ Change detection
/ Classification
/ Comparative analysis
/ data collection
/ Deep learning
/ Image processing
/ Machine learning
/ Methods
/ multispectral imaging
/ Multispectral photography
/ Neural networks
/ optical imaging
/ Remote sensing
/ Semantics
/ Sensors
/ Technology application
/ Urban planning
2023
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A Review of Deep-Learning Methods for Change Detection in Multispectral Remote Sensing Images
Journal Article
A Review of Deep-Learning Methods for Change Detection in Multispectral Remote Sensing Images
2023
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Overview
Remote sensing is a tool of interest for a large variety of applications. It is becoming increasingly more useful with the growing amount of available remote sensing data. However, the large amount of data also leads to a need for improved automated analysis. Deep learning is a natural candidate for solving this need. Change detection in remote sensing is a rapidly evolving area of interest that is relevant for a number of fields. Recent years have seen a large number of publications and progress, even though the challenge is far from solved. This review focuses on deep learning applied to the task of change detection in multispectral remote-sensing images. It provides an overview of open datasets designed for change detection as well as a discussion of selected models developed for this task—including supervised, semi-supervised and unsupervised. Furthermore, the challenges and trends in the field are reviewed, and possible future developments are considered.
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