A Systematic Review of Machine Learning Methods for Change Detection in ‎Radar and Optical Images with a Focus on Deep Learning

Zargar, M.R.; Aghabalaei, A.; Khazai, S.a; Mohtadi, A.

doi:10.22061/jrsgr.2025.12190.1104

Articles in Press

Document Type : Review Paper

Authors

¹ Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, University of ‎Tehran, Tehran, Iran

² Department of Surveying and Spatial Data, Faculty of Civil Engineering, Water and Energy, Imam Hossein Comprehensive ‎University, Tehran, Iran

³ Department of Signature engineering, Faculty of Passive Defense, Imam Hossein Comprehensive University, Tehran, Iran

⁴ Department of Surveying Engineering, Faculty of Technology and Engineering, Hekmat Higher Educational Institute, Qom, ‎Iran

https://doi.org/10.22061/jrsgr.2025.12190.1104

Abstract

Background and Objectives: With the expansion of radar remote sensing data and increased access to high-resolution imagery through sensors such as Sentinel-1, change detection using deep learning has emerged as a strategic and innovative field in geospatial sciences. Radar imagery, with its capabilities for day-and-night imaging, cloud penetration, and sensitivity to structural characteristics of the Earth’s surface, provides rich but complex data requiring advanced machine learning architectures for effective analysis. Accordingly, this study aims to systematically review deep learning-based methods for change detection in radar images, with a focus on comparative analysis of architectures, their strengths and limitations, and future research directions.
Methods: This systematic review covers literature published between 2014 and 2025 and includes 44 selected studies from reputable databases such as IEEE, Elsevier, and MDPI. Inclusion criteria involved the use of SAR data, application of deep learning algorithms, availability of quantitative performance metrics (e.g., accuracy and F1-score), and operational relevance in domains such as urban monitoring, natural resource assessment, and disaster management. The studies were classified based on the type of learning approach (supervised, unsupervised, self-supervised, multi-source) and architecture used (MLP, CNN, U-Net, Autoencoder, LSTM, GAN, MSCDUNet), and were analyzed using comparative tables.
Findings: The results indicate that supervised architectures such as U-Net performed best in urban and disaster-related applications, achieving up to 95% accuracy and F1-scores between 0.85 and 0.93. In unsupervised approaches, combining CNN with fuzzy clustering (FCM) reached accuracy levels up to 99.6%. Autoencoder-based models were successful in denoising and feature compression, while GAN architectures improved network performance through data augmentation. Multi-source models like MSCDUNet, integrating radar and optical data, reported F1-scores of up to 0.93. However, challenges persist, including inconsistent reporting of standard metrics such as F1, limited generalizability of models, and the computational complexity of processing heterogeneous datasets.
Conclusion: Despite significant advancements in the use of deep learning for change detection, ongoing challenges include the scarcity of labeled data, lack of publicly available benchmark multi-source datasets, and the limited availability of lightweight algorithms for real-time applications. Future research should prioritize self-supervised methods such as contrastive learning, the development of noise-resistant and lightweight architectures for UAV and edge deployments, and the creation of standardized open-access datasets with comprehensive metrics. This study, by offering a structured classification and comparative evaluation of algorithms, aims to inform intelligent decision-making in the design of change detection systems for researchers and developers alike.

Keywords

Main Subjects

Remote Sensing

COPYRIGHTS

(https://creativecommons.org/licenses/by-nc/4.0/)

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Journal of Remote Sensing and Geoinformation Research

A Systematic Review of Machine Learning Methods for Change Detection in ‎Radar and Optical Images with a Focus on Deep Learning

References

References

Articles in Press, Accepted Manuscript
Available Online from 13 September 2025

A Systematic Review of Machine Learning Methods for Change Detection in ‎Radar and Optical Images with a Focus on Deep Learning

References

References

Articles in Press, Accepted Manuscript Available Online from 13 September 2025

Articles in Press, Accepted Manuscript
Available Online from 13 September 2025