Deep Learning for SAR Target Detection and Instance Segmentation
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 218
Editors
Interests: radar signal processing; sar image interpretation; remote sensing image processing
Interests: intelligent sensors; machine learning; data analytics; information fusion; IoT
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing information processing; synthetic aperture radar (SAR) image interpretation; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Synthetic aperture radar (SAR) has become an essential remote sensing technology due to its all-weather, day-and-night, and high-resolution imaging capabilities. SAR imagery is widely used in maritime surveillance, traffic monitoring, disaster response, urban observation, environmental monitoring, and defense-related applications. In these scenarios, accurate target detection and instance segmentation are crucial for obtaining fine-grained information, including target location, category, shape, orientation, and spatial distribution.
Recent advances in deep learning have greatly promoted SAR image interpretation by enabling powerful feature representation, end-to-end optimization, and adaptive modeling of complex scattering characteristics. Object detection networks, instance segmentation frameworks, attention mechanisms, transformer architectures, self-supervised learning, few-shot learning, and multimodal fusion have shown potential to improve the accuracy, robustness, and automation of SAR target analysis. However, SAR target detection and instance segmentation remain challenging due to speckle noise, complex backgrounds, shadowing effects, geometric distortion, dense target distribution, multiscale targets, and limited annotated datasets. Practical applications also require models with strong generalization ability, computational efficiency, interpretability, and robustness under diverse imaging conditions.
This Special Issue will focus on the recent progress, technical challenges, and future directions in deep-learning-based SAR target interpretation. Topics of interest include, but are not limited, to the following topics for efficient and advanced SAR target detection and instance segmentation: Small, oriented, and dense SAR target detection methods; Transformer- and attention-based detection and instance segmentation models; Few-shot learning, domain adaptation, and multimodal fusion for SAR applications; Physics-guided and interpretable deep learning models; Lightweight networks and benchmark datasets; Foundation models for large-scale SAR applications.
Dr. Yingying Kong
Prof. Dr. Henry Leung
Prof. Dr. Kefeng Ji
Dr. Jinglu He
Guest Editors
Manuscript Submission Information
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Keywords
- synthetic aperture radar (SAR)
- SAR target detection
- instance segmentation
- deep learning
- physics-guided learning
- attention mechanisms
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