A Survey on Change Detection in Synthetic Aperture Radar Satellite Images

DOI

10.2991/978-94-6463-716-8_40How to use a DOI?

Keywords

SAR; change detection; Sentinel-1; PCA; FCM clustering; GIS applications; CVA; CACo

Abstract

Change detecting in Synthetic Aperture Radar (SAR) satellite images has garnered significant attention for its applications in urban planning, disaster management, and environmental monitoring. SAR’s ability to operate under all weather and lighting conditions makes it indispensable for monitoring dynamic changes on Earth’s surface. However, distinguishing man-made changes from natural variations, such as vegetation growth or seasonal water fluctuations, remains a critical challenge. This survey explores recent advancements and methodologies in change detection for multi-temporal SAR images, focusing on hybrid approaches that integrate traditional techniques and deep learning. Key methods, including Change Vector Analysis (CVA), Principal Component Analysis (PCA), and Fuzzy C-Means (FCM), are discussed alongside emerging techniques like self-supervised learning and contrastive loss functions designed to minimize false positives. We review experimental results from the Sentinel-1 dataset, highlighting trends, strengths, and limitations of existing approaches. Outputs in standard formats such as GeoJSON or shapefiles demonstrate their utility for GIS-based real-time monitoring systems. By providing a comprehensive overview, this paper aims to inform future research and development of scalable, accurate solutions for change detection in SAR remote sensing applications.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Saili Sable
AU  - Omkar Unde
AU  - Deepak Singh
AU  - Aditya Jadhav
PY  - 2025
DA  - 2025/05/26
TI  - A Survey on Change Detection in Synthetic Aperture Radar Satellite Images
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 513
EP  - 525
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_40
DO  - 10.2991/978-94-6463-716-8_40
ID  - Sable2025
ER  -