SAR Image Change Detection: From Hand-Crafted to Deep Learning
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 28 September 2025 | Viewed by 52
Special Issue Editors
Interests: remote sensing image registration; change detection; 3D reconstruction
Interests: remote sensing image change detection; image segmentation
Special Issues, Collections and Topics in MDPI journals
Interests: satellite image geometric processing; image mosaics; radiometric normalization; image matching; SAR change detection
Special Issue Information
Dear Colleagues,
Synthetic Aperture Radar (SAR) image change detection has emerged as a pivotal technology in modern remote sensing, driven by SAR's unique capability to penetrate cloud cover and operate independently of daylight, enabling uninterrupted Earth observation. By analyzing temporal differences in multi-temporal SAR imagery, this technique identifies critical landscape alterations, including deforestation, urban expansion, agricultural dynamics, and post-disaster damage. Traditional approaches, predominantly relying on hand-crafted algorithms such as ratio operators, and statistical models, frequently struggle with SAR's inherent challenges—speckle noise, geometric distortions, and intricate backscattering mechanisms. These limitations often result in reduced accuracy, particularly in heterogeneous environments, and necessitate labor-intensive parameter tuning. The paradigm shift toward deep learning (DL) has revolutionized SAR change detection by automating hierarchical feature extraction and enhancing model robustness. Convolutional Neural Networks (CNNs) and advanced architectures like U-Net and Transformer-based models excel at capturing multi-scale spatiotemporal patterns, effectively suppressing noise while preserving subtle change signatures. Innovations such as Siamese networks, attention mechanisms, and unsupervised learning frameworks further address critical bottlenecks like labeled data scarcity and cross-domain adaptation, enabling scalable deployment across diverse geographical regions. This transition not only elevates detection precision but also unlocks near-real-time monitoring capabilities, which are indispensable for rapid disaster response and sustainable resource management.
Despite these advancements, challenges persist in SAR change detection, primarily due to SAR-specific complexities: persistent speckle noise interference, the high cost of acquiring accurately labeled training data, and inconsistencies in heterogeneous data representations across multi-source sensors. Additionally, the integration of SAR with complementary remote sensing modalities (e.g., optical, LiDAR) remains underexplored, limiting the full exploitation of multi-modal data synergies.
This Special Issue aims to consolidate cutting-edge advancements, innovative methodologies, and practical applications in SAR image change detection, including, but not limited to, the following:
- Deep learning for SAR change detection;
- Multi-temporal analysis for SAR data;
- Dataset and evaluation for deep learning-based SAR image change detection methods;
- Multi-modal image change detection;
- Remote sensing applications based on SAR change detection.
Suggested themes and article types for submissions.
Research Articles, Letters, Technical Notes, Review Articles
Dr. Yuming Xiang
Dr. Ling Wan
Dr. Niangang Jiao
Dr. Sourabh Paul
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning for SAR image change detection
- SAR change detection applications
- multi-modal image change detection
- multi-temporal data analysis
- SAR change detection datasets
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