Deep Learning for Intelligent Synthetic Aperture Radar Systems
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 2017
Special Issue Editors
Interests: multisensor remote sensing; change detection; uncertainty quantification
Special Issues, Collections and Topics in MDPI journals
Interests: multi-source data fusion; target detection
Interests: 3D remote sensing; SAR building detection; uncertainty quantification
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Synthetic aperture radar (SAR) is an important active remote sensing sensor that provides all-day, all-weather imaging capability. It has been extensively used in different applications, e.g., change detection, target detection, disaster management, and geological exploration. SAR images come in different spatial/temporal resolutions, frequencies, and polarizations. Like passive images, deep learning has recently gained popularity in the analysis of active SAR images as well. However, due to less visual saliency, the presence of speckle noise, and significant variation among different SAR systems, applications of deep-learning-based methods in this data domain are often not straightforward. Furthermore, unlike optical images, most SAR applications are focused on target detection, structural changes, and 3D reconstruction. Methods and architectures originally designed for mere classification and semantic segmentation require additional modeling to be used for such applications. Thus, there are still many research issues in the deep-learning-based analysis of SAR data that require the attention of the research community.
This Special Issue aims to collect and highlight contributions focusing on novel deep-learning-based methods and architectures that can particularly address the challenges faced in SAR image/data analysis or multi-sensor systems with SAR as one of the sensors. Starting from more popular topics like target detection and change detection, this Special Issue also aims to collect contributions from emerging topics, such as uncertainty quantification.
Suggested themes:
- SAR target detection;
- SAR change detection;
- SAR image classification;
- SAR semantic segmentation;
- Uncertainty quantification;
- Multisensory approaches.
Dr. Sudipan Saha
Dr. Taoyang Wang
Dr. Muhammad Shahzad
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
- deep-learning-based SAR imaging
- target detection
- SAR image classification
- SAR change detection
- SAR 3D reconstruction
- SAR uncertainty quantification
