The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Research works printed but not linked: 15
- goodfellow, 2016, Deep Learning
- 10.1007/s41207-016-0007-4 · doi:10.1007/s41207-016-0007-4
- jordan, 2018, Common Architectures in Convolutional Neural Networks
- suykens, 1999, Least squares support vector machine classifiers: A large scale algorithm, Proc Eur Conf Circuit Theory Design (ECCTD), 839
- buckner, 2019, Connectionism, The Stanford Encyclopedia of Philosophy
- malila, 1980, Change vector analysis: An approach for detecting forest changes with landsat, Proc LARS Symp, 385
- 10.1016/j.jvcir.2019.102585 · doi:10.1016/j.jvcir.2019.102585
- 10.1109/91.811242 · doi:10.1109/91.811242
- he, 2015, Deep residual learning for image recognition, arXiv 1512 03385
- 10.1007/s11220-019-0252-0 · doi:10.1007/s11220-019-0252-0
- 10.1109/TGRS.2007.895835 · doi:10.1109/tgrs.2007.895835
- zhang, 2018, Change detection between multimodal remote sensing data using siamese CNN, arXiv 1807 09562
- 10.1016/j.landusepol.2006.02.002 · doi:10.1016/j.landusepol.2006.02.002
- 10.3390/app9071441 · doi:10.3390/app9071441
- 10.3390/rs9050435 · doi:10.3390/rs9050435
Cited by
A review on deep learning in UAV remote sensing
Osco, L. P., Marcato Junior, J., Marques Ramos, A. P., de Castro Jorge, L. A., Fatholahi, S. N., de Andrade Silva, J., Matsubara, E. T., Pistori, H., Gonçalves, W. N., & Li, J. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102456 · Cited by 458
Self-Supervised Learning in Remote Sensing: A review
Wang, Y., Albrecht, C. M., Braham, N. A. A., Mou, L., & Zhu, X. X. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2022.3198244 · Cited by 364
Deep learning for change detection in remote sensing: a review
Bai, T., Wang, L., Yin, D., Sun, K., Chen, Y., Li, W., & Li, D. (2023) · Geo-spatial Information Science · doi:10.1080/10095020.2022.2085633 · Cited by 328
Satellite On-Board Change Detection via Auto-Associative Neural Networks
Guerrisi, G., Del Frate, F., & Schiavon, G. (2022) · Remote Sensing · doi:10.3390/rs14122735 · Cited by 8
Anomaly Detection in Spacecraft Telemetry Using Vision Transformer and Caps-LSTM
Rajesh, S., Jarin, T., Emin Thomas, C., & Jaison, J. (2025) · 2025 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) · doi:10.1109/accthpa65749.2025.11168608
Metadata
- Authors
- Khelifi, L., & Mignotte, M.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 126385-126400
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3037640242
- Cited by
- 441
- References
- 116
- FWCI
- 27.15
- Type
- journal article
- Language
- en
- Open access
- gold
- Funders
- Natural Sciences and Engineering Research Council of Canada
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis. https://app.cochat.ai/papers/10.1109/access.2020.3008036
- The paper
- Khelifi, L., & Mignotte, M. (2020). Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis. IEEE Access. https://doi.org/10.1109/access.2020.3008036
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
