Advances in AI-Driven Synthetic Aperture Radar (SAR): Data Processing to Automatic Interpretation
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 15 November 2025 | Viewed by 47
Special Issue Editors
Interests: synthetic aperture radar (SAR) signal processing; SAR image interpretation; high performance computing; SAR/optical remote sensing image processing; Artificial intelligence
Interests: advanced spaceborne SAR signal processing; remote sensing information extraction
Special Issues, Collections and Topics in MDPI journals
Interests: two-dimensional (2D)/three-dimensional (3D) microwave imaging systems; image processing; signal processing and applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Synthetic aperture radar (SAR) plays an indispensable role in modern remote sensing, providing robust imaging capabilities under diverse environmental and illumination conditions. Recent advances in artificial intelligence (AI), particularly in machine learning and data-driven modeling, have demonstrated significant promise in enhancing SAR data processing, facilitating automatic interpretation and broadening the scope of SAR applications.
This Special Issue invites contributions that explore how AI technologies are reshaping SAR data processing and interpretation, covering the full spectrum from foundational advances in signal and image processing to high-level semantic understanding and automated interpretation. Emphasis will be placed on approaches that not only advance AI methodology, but which also push the boundaries of remote sensing capabilities by leveraging the unique characteristics of SAR data. Topics may include, but are not limited to, the following:
- AI-based SAR image enhancement and denoising;
- Intelligent classification and segmentation of SAR imagery;
- Target detection and recognition from SAR data;
- Learning-based interferometric and polarimetric SAR processing;
- AI-driven multi-sensor data fusion with SAR;
- Domain adaptation and generalization across SAR scenes;
- Large-scale SAR data analytics and automated mapping.
We look forward to receiving your contributions.
Prof. Dr. Fan Zhang
Prof. Dr. Bing Han
Prof. Dr. Weixian Tan
Dr. Qiang Yin
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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
- synthetic aperture radar (SAR)
- AI-based synthetic aperture radar image processing
- deep learning in remote sensing
- automatic target recognition (ATR)
- InSAR and PolSAR interpretation
- synthetic aperture radar data fusion
- semantic segmentation of synthetic aperture radar
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