Radar Image Understanding Using Vision-Inspired Deep Learning with Sensor Integration
A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Sensing and Imaging".
Deadline for manuscript submissions: 15 October 2026 | Viewed by 343
Editors
Interests: novel imaging systems (including computational and compressive imaging), machine vision and pattern recognition systems, infrared and RF automatic target recognition
Special Issue Information
Dear Colleagues,
Synthetic Aperture Radar (SAR) is a form of active sensor used for imaging at long ranges. SAR sensors are widely used on airborne platforms for geo-sensing, surveillance and security, and for target detection/recognition. In recent years, the field of machine learning (ML) has advanced rapidly with the advent of foundation models, self-supervised learning, multimodal fusion, and learning with limited labels. While these methods have been widely used for computer vision, their application for analyzing SAR imagery is comparatively rare. However, there is considerable interest in analyzing SAR imagery using deep learning and ML techniques originally developed for computer vision. For this Special Issue, we therefore seek papers which deal with the application of advanced ML techniques for analyzing and understanding SAR imagery. Topics of interest include, but are not limited to:
- Robust learning and foundation models for SAR imagery;
- Remote sensing and terrain analysis;
- Automatic target detection and recognition (including targets on land and at sea);
- Generation and use of synthetic SAR imagery for training and algorithm evaluations;
- Spatiotemporal learning and sequential decision-making using SAR;
- Fusion of SAR and other sensing modalities.
Dr. Abhijit Mahalanobis
Guest Editor
Dr. Banafsheh Saber Latibari
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- radar imaging
- synthetic aperture radar (SAR)
- efficient deep learning
- robustness and uncertainty estimation
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