Computer Vision, Neural Networks and Deep Learning for SAR Image Processing
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 October 2025 | Viewed by 27
Special Issue Editors
Interests: synthetic aperture radar; SAR; SAR image classification; SAR image interpretation with AI; polarimetric SAR; deformation monitoring; vegetation mapping
Interests: time-series PolInSAR data processing and its applications in geosciences, including statistical scattering signal modeling, multidimensional SAR image filtering, interferometric phase optimization, surface deformation monitoring, and forest parameter inversion
Interests: synthetic aperture radar (SAR); computational imaging; inverse problems; statistical signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Synthetic Aperture Radar (SAR) is a widely used remote sensing technology capable of capturing high-resolution images under all weather conditions and during both day and night. However, SAR image processing presents unique challenges due to speckle noise, geometric distortions, and complex scattering mechanisms. Traditional methods often struggle with these complexities, prompting the need for advanced techniques. Recent advances in computer vision, neural networks (NNs), and deep learning (DL) have revolutionized SAR image interpretation. Convolutional Neural Networks (CNNs), Transformers, Generative Adversarial Networks (GANs), and other DL architectures have shown remarkable success in tasks of SAR data processing. This Special Issue seeks high-quality research contributions that leverage computer vision, NN, and DL techniques to address SAR image processing challenges. Potential topics include (but are not limited to) the following: SAR image classification and segmentation, object detection, change detection, parameter inversion, multi-modal fusion with SAR/optical/LiDAR data, agricultural remote sensing, forest application, soil moisture inversion, geohazard monitoring, and disaster response. This Special Issue aims to compile state-of-the-art methodologies, providing a reference for future developments in AI-driven SAR image processing.
Prof. Dr. Changcheng Wang
Dr. Peng Shen
Prof. Dr. Alin Achim
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
- SAR image processing
- computer vision
- deep learning
- object detection with SAR
- SAR image classification
- PolSAR classification
- optical and SAR data fusion
- agricultural monitoring with SAR
- forest monitoring with SAR
- soil moisture inversion with SAR
- geohazard monitoring with SAR
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