Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis

By Lazhar Khelifi, Max Mignotte. IEEE Access, 2020

350 citations recorded by the source corpus.

Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which are frequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of change detection DL studies. Then, we focus on deep learning-based change detection methodologies for remote sensing images by giving a general overview of the existing methods. Specifically, these deep learning-based methods were classified into three groups; fully supervised learning-based methods, fully unsupervised learning-based methods and transfer learning-based techniques. As a result of these investigations, promising new directions were identified for future research. This study will contribute in several ways to our understanding of deep learning for change detection and will provide a basis for further research. Some source codes of the methods discussed in this paper are available from: https://github.com/lazharkhelifi/deeplearning_changedetection_remotesensing_review.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/65220c1f731aa091febdad76f378bba44a588530

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Le Wang
2. Ting Bai
3. Jonathan Li
4. Xiao Xiang Zhu
5. Deren Li
6. Wenzhuo Li
7. Yepei Chen
8. Dameng Yin

In Ecology

1. Le Wang
2. Xiao Xiang Zhu
3. Lichao Mou

In Media Technology

1. Le Wang
2. Conrad M. Albrecht
3. Wesley Nunes Gonçalves
4. Lucas Prado Osco
5. Ting Bai
6. Jonathan Li
7. Xiao Xiang Zhu
8. Deren Li

Citations in the corpus

• A Review on Deep Learning in UAV Remote Sensing (2021, background): “In more of a “niche” type of research, environmental applications and land surface change detection were investigated in literature revision papers by Yuan et al. [201] and Khelifi et al. [106], respectively.”
• Self-Supervised Learning in Remote Sensing: A review (2022, background): “We refer the interested reader to the numerous surveys on machine learning and deep learning in remote sensing [8, 239, 240, 241, 242, 243, 244] for more details about common downstream tasks and available datasets.”
• Deep learning for change detection in remote sensing: a review (2022, background): “Zhu et al. (2017) review of the advance of deep learning for change detection (Shi et al. 2020; Khelifi, and Mignotte 2020).”