Deep Learning for Target Detection in Radar Remote Sensing
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 August 2026 | Viewed by 241
Special Issue Editors
Interests: synthetic aperture radar (SAR); image processing; feature extraction; automatic target detection and recognition; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: synthetic aperture radar; perturbation methods; target recognition; image segmentation; feature extraction; deep learning
Interests: electromagnetic wave propagation; machine learning; remote sensing; radar image analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As an active microwave remote sensing system, radar plays an irreplaceable role in critical fields such as air traffic control, meteorological observation, and autonomous driving, thanks to its all-weather and all-time operational capabilities. However, in the face of increasingly complex operational environments, traditional radar detection methods have gradually reached their performance limits in terms of weak signal extraction, complex clutter suppression, and scene adaptability. Moreover, with the rapid development of high-resolution, multi-band, and multi-polarization radar technologies, radar remote sensing data have shown characteristics of being massive, high-dimensional, and complex. Traditional radar signal processing methods encounter bottlenecks in terms of accuracy and efficiency when dealing with nonlinear features and complex scenes. Over the past few years, deep learning techniques, with their end-to-end learning approach, have brought about revolutionary breakthroughs in radar remote sensing signal processing. By establishing a deep neural network model for radar target detection tasks, it is possible to automatically extract the deep separability features of the targets from the massive radar data, enabling intelligent detection and identification of targets in low signal-to-noise ratio and complex environments, significantly enhancing the robustness and adaptability of the system. Therefore, the deep integration of deep learning and radar detection technology is not only the inevitable path to break through the existing technical bottlenecks, but also the key driving force for the evolution of radar systems towards intelligence and cognition.
This Special Issue aims to bring together the related advanced research in deep learning for target detection in radar remote sensing. We sincerely invite you to contribute your latest research findings to this issue. Both original research articles and review papers are welcome.
Research areas may include (but are not limited to) the following:
- Radar marine target detection.
- Satellite/airborne SAR/ISAR image target detection and recognition.
- High-resolution range profile target recognition.
- Multi-source/multi-modal information fusion (radar, optical, etc.) for target detection and recognition.
- Detection of moving dim targets on airborne radar.
- GNSS-based radar remote target detection.
- Interpretability of deep learning in radar target detection and target recognition.
Dr. Haohao Ren
Dr. Chuan Du
Dr. Ming Li
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- radar target detection
- radar target recognition
- multi-source fusion-based target detection
- deep learning
- explainability
- SAR image
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