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Remote Sensing
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Recent Advances in SAR: Signal Processing and Target Recognition
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Recent Advances in SAR: Signal Processing and Target Recognition

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 10 September 2025 | Viewed by 801

Special Issue Editors

Prof. Dr. Dahai Dai

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Guest Editor
School of Electronic Science, National University of Defense Technology, Changsha 410073, China
Interests: radar system design; signal and image processing; machine learning
Dr. Fengming Hu

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Guest Editor
School of Information Science and Engineering, Fudan University, Shanghai, China
Interests: multi-temporal synthetic aperture radar interferometry (MT-InSAR); SAR; change detection; deformation Interpretation

Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) has emerged as a critical technology across various fields, including space reconnaissance, remote sensing, resource exploration, ocean monitoring, and non-destructive testing, due to its all-weather, all-day observation capabilities and high-resolution imaging. However, challenges such as signal noise and interference, the complexity of target structures, dynamic changes in environmental factors, the balance between resolution and processing efficiency, the need for multi-source data fusion, and the need for adaptability to cross-disciplinary applications all pose significant hurdles to the quality of SAR signal processing and the accuracy of target recognition.

In response to these challenges, this Special Issue focuses on the field of SAR signal processing and target recognition, aiming to gather and highlight the latest research achievements and innovative practices in the field. This includes, but is not limited to, the following topics:

  • Novel SAR signal denoising and filtering techniques;
  • High-resolution SAR image detection and reconstruction algorithms;
  • Deep learning-based SAR target recognition methods;
  • Multi-polarization SAR data fusion and feature extraction;
  • Change detection and time series analysis in SAR images;
  • SAR target detection and tracking in complex environments;
  • Resolution and efficiency optimization in SAR signal processing;
  • Fusion technology of multi-source remote sensing data with SAR data;
  • SAR signal processing algorithms tailored for specific application scenarios;
  • Cross-disciplinary applications of SAR signal processing and target recognition research.

Prof. Dr. Dahai Dai
Dr. Fengming Hu
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
  • signal processing
  • target recognition
  • machine learning
  • radar system
  • SAR feature extraction and processing

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Published Papers (2 papers)

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Research

18 pages, 3678 KiB  
Article
Performance Degradation in Monopulse Angle Measurement of Planar Phased-Array Due to Cross-Polarization Component
by Yunhui Zhang, Bo Pang, Dahai Dai, Bo Chen and Zhengkuan Tan
Remote Sens. 2025, 17(14), 2454; https://doi.org/10.3390/rs17142454 - 15 Jul 2025
Viewed by 191
Abstract
Due to the high-precision angle measurement performance, the monopulse technique plays a key role in fields such as remote sensing and space surveillance. The accuracy of monopulse angle measurement depends on the received amplitude and phase information, which is sensitive to the polarization [...] Read more.
Due to the high-precision angle measurement performance, the monopulse technique plays a key role in fields such as remote sensing and space surveillance. The accuracy of monopulse angle measurement depends on the received amplitude and phase information, which is sensitive to the polarization component. Previous research has demonstrated that the performance of monopulse radar equipped with a parabolic antenna suffers from the cross-polarization component. However, it is not clear whether phased arrays (PAs) with higher degrees of freedom will also be affected by the cross-polarization component, and the parameter tolerance for performance degradation remains uncertain. In this paper, we establish a mathematical model of monopulse angle measurement in PA radar, which provides a comprehensive consideration of the cross-polarization component. Then, the received amplitude and phase patterns of PA radar are analyzed, and the theoretical angle errors caused by the cross-polarization jamming are derived. The experiments are conducted based on the measured amplitude-phase patterns of both co-polarization and cross-polarization. Experimental results are consistent with the theoretical analysis: the angle errors caused by cross-polarization jamming can reach half of the beamwidth in both azimuth and elevation dimensions, provided that the power of the cross-polarization and co-polarization components at the receiver is equal. Full article
(This article belongs to the Special Issue Recent Advances in SAR: Signal Processing and Target Recognition)
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27 pages, 6636 KiB  
Article
SCF-CIL: A Multi-Stage Regularization-Based SAR Class-Incremental Learning Method Fused with Electromagnetic Scattering Features
by Yunpeng Zhang, Mengdao Xing, Jinsong Zhang and Sergio Vitale
Remote Sens. 2025, 17(9), 1586; https://doi.org/10.3390/rs17091586 - 30 Apr 2025
Cited by 1 | Viewed by 370
Abstract
Synthetic aperture radar (SAR) recognition systems often need to collect new data and update the network accordingly. However, the network faces the challenge of catastrophic forgetting, where previously learned knowledge might be lost during the incremental learning of new data. To improve the [...] Read more.
Synthetic aperture radar (SAR) recognition systems often need to collect new data and update the network accordingly. However, the network faces the challenge of catastrophic forgetting, where previously learned knowledge might be lost during the incremental learning of new data. To improve the applicability and sustainability of SAR target classification methods, we propose a multi-stage regularization-based class-incremental learning (CIL) method for SAR targets, called SCF-CIL, which addresses catastrophic forgetting. This method offers three main contributions. First, for the feature extractor, we fuse the convolutional neural network features with the scattering center features using a cross-attention feature fusion structure, ensuring both the plasticity and stability of the extracted features. Next, an overfitting training strategy is applied to provide clustering space for unseen classes with an acceptable trade-off in the accuracy of the current classes. Finally, we analyze the influence of training with imbalanced data on the last fully connected layer and introduce a multi-stage regularization method by dividing the calculation of the fully connected layer into three parts and applying regularization to each. Our experiments on SAR datasets demonstrate the effectiveness of these improvements. Full article
(This article belongs to the Special Issue Recent Advances in SAR: Signal Processing and Target Recognition)
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