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Remote Sensing
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Recent Advances in SAR Images for Target Detection and Information...
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Recent Advances in SAR Images for Target Detection and Information Extraction

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 412

Editors

Dr. Shiyu Luo

E-Mail Website
Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: SAR image processing and analysis; scattering mechanism modelling and inversion; deep learning; computer vision
Dr. Luca Pallotta

E-Mail Website
Guest Editor
Department of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: radar target recognition; covariance matrix estimation; adaptive radar receivers; SAR Images; optical imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Wang

E-Mail Website
Guest Editor
No. 365 Institute, Northwestern Polytechnical University, Xi'an 710129, China
Interests: multi-source information fusion processing; Intelligent SAR target detection
Dr. Ming Li

E-Mail Website
Guest Editor
National Center for Atmospheric Research, University Corporation for Atmospheric Research, Boulder, CO, USA
Interests: electromagnetic wave propagation; machine learning; remote sensing; radar image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The accurate and automated recognition of targets and extraction of information from Synthetic Aperture Radar (SAR) imagery are critical for advancing Earth observation and surveillance capabilities. This field has evolved significantly from early foundational studies to the current era of high-resolution and multi-dimensional data. While this development enhances the precision of observation, it also presents new challenges. These include the modeling of complex scattering mechanisms, the semantic gap between radar measurements and physical interpretation, and performing reliable recognition under low signal-to-noise ratios or with limited labeled data. To advance the technology, it is necessary to adopt innovative approaches that can leverage physics-based modeling, advanced statistical methods, and artificial intelligence technologies. These approaches can be applied individually or through synergistic integration to achieve their full potential.

This Special Issue aims to collect studies that advance the field of target recognition and information extraction from Synthetic Aperture Radar (SAR) imagery. We welcome contributions that explore novel methodologies and applications across various scales and complexities. Research integrating physical scattering models with advanced artificial intelligence, utilizing multi-dimensional SAR data, and addressing challenges in complex scenarios is highly encouraged. Topics of interest include, but are not limited to, the following:

  • Physics-inspired deep learning models for SAR target recognition and scattering mechanism inversion
  • Land cover classification and target detection in polarimetric SAR for complex scenarios
  • Semantic and instance segmentation in high-resolution SAR imagery
  • Change detection and dynamic monitoring using time-series SAR data
  • SAR target detection under low signal-to-noise ratio and weakly supervised conditions

Dr. Shiyu Luo
Dr. Luca Pallotta
Dr. Jian Wang
Dr. Ming Li
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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)
  • target detection
  • information extraction
  • physics-inspired deep learning
  • scattering mechanism inversion
  • semantic segmentation
  • multi-dimensional SAR
  • weakly supervised learning

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Published Papers (1 paper)

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Research

32 pages, 14709 KB  
Article
Minimizing Peak Sidelobe Level in MIMO-SAR Waveform Design Using a 1.5-Entmax Sparse Loss
by Wentao Li, Shujuan Tang, You Chen, Zhuoluo Wang and Siyi Cheng
Remote Sens. 2026, 18(14), 2409; https://doi.org/10.3390/rs18142409 - 20 Jul 2026
Viewed by 39
Abstract
The peak sidelobe level (PSL) of an orthogonal waveform set directly governs the range and azimuth ambiguities of synthetic aperture radar (SAR) images and is therefore a key figure of merit for the imaging quality of multiple-input multiple-output SAR (MIMO-SAR). Minimizing the PSL [...] Read more.
The peak sidelobe level (PSL) of an orthogonal waveform set directly governs the range and azimuth ambiguities of synthetic aperture radar (SAR) images and is therefore a key figure of merit for the imaging quality of multiple-input multiple-output SAR (MIMO-SAR). Minimizing the PSL is an NP-hard problem with a non-differentiable objective, and existing approaches often suffer from high computational cost and limited scalability while achieving only suboptimal PSL suppression. We therefore propose a loss function built around the 1.5-entmax sparse transform, which is simultaneously differentiable, sparse, and adaptive. Analysis of its differentiability and derivation of its analytical gradient allow us to recast the NP-hard problem as a differentiable optimization problem that can be solved by well-established algorithms. Owing to the sparsity of the transform, the gradient is concentrated on the high-energy sidelobes, while the gradient contribution from low-energy sidelobes becomes exactly zero, removing the gradient noise contributed by low-energy sidelobes and yielding a lower PSL. To overcome the scale sensitivity of the 1.5-entmax function, the upper bound on the sidelobe magnitude is used to normalize the input, which makes the threshold adaptive and removes the need for additional loss-function hyperparameter tuning across waveform-design tasks of different sizes. To further improve computational efficiency, we combine gradient descent with a deep learning framework: the waveform phases are treated as the learnable parameters of a neural network-like model, thereby yielding a back-propagation-based optimization framework with graphics processing unit (GPU) parallelism. The algorithm is implemented for parallel execution on GPU, and the gradient is computed efficiently through the network’s automatic differentiation in conjunction with a custom analytical-gradient operator, leading to a substantial increase in computational speed. Without the need for the manual tuning of loss-function hyperparameters, the proposed algorithm achieves the lowest PSL across waveform sets of various sizes and reduces computation time by approximately two orders of magnitude in large-scale settings. Full article
(This article belongs to the Special Issue Recent Advances in SAR Images for Target Detection and Information Extraction)
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