A Review of Deep-Learning Methods for Change Detection in Multispectral Remote Sensing Images
Om publikasjonen
: 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.
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.
Utgiverinformasjon
Parelius Jonasova, Eleonora. A Review of Deep-Learning Methods for Change Detection in Multispectral Remote Sensing Images. Remote Sensing 2023 ;Volum 15.(8)