Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.6
4.1
Journals
Remote Sensing
Special Issues
Advances in Synthetic Aperture Radar (SAR) Imaging and Signal Processing...
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Synthetic Aperture Radar (SAR) Imaging and Signal Processing Technologies

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 May 2026 | Viewed by 2411

Special Issue Editors

Dr. Rui Guo

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical Universtiy, Xi’an 710129, China
Interests: radar signal processing; radar image processing; radar point cloud processing; radar and laser in remote sensing applications
Special Issues, Collections and Topics in MDPI journals
Dr. Huizhang Yang

E-Mail Website
Guest Editor
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: radar image processing; radar interferometry; target detection; jamming suppression; polarimetric radar
Dr. Lianhuan Wei

E-Mail Website
Guest Editor
College of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
Interests: microwave remote sensing and applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuxiao Qin

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China
Interests: SAR; InSAR; multi-temporal InSAR; signal processing
Dr. Jian Kang

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
Interests: InSAR; SAR image processing; intelligent interpretation of SAR images based on deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic Aperture Radar (SAR) stands as a pivotal sensor within the earth observation systems, continually driving advancements in remote sensing technology. It plays a critical role across numerous fields, including environmental monitoring, resource exploration, disaster assessment, and security surveillance. In recent years, the ongoing evolution of SAR imaging technologies has presented new opportunities and challenges in the domain of signal and image processing. To further unlock the potential of SAR data and enhance the accuracy and efficiency of information extraction, this Special Issue aims to collate the latest research findings from scholars on cutting-edge progress in SAR signal and image processing.

This Special Issue will focus on innovations and breakthroughs in SAR signal and image processing methodologies. Specifically, it includes (but is not limited to) novel imaging modes, advanced signal processing methods, intelligent image interpretation approaches, and their innovative applications across diverse sectors.

Potential topics for this Special Issue include, but are not limited to, the following:

  • Novel SAR 2D/3D/4D Imaging;
  • SAR System and Waveform Design;
  • Polarimetric SAR and application;
  • Image classification and target detection/recognition methods for SAR;
  • SAR Image Quality Enhancement and Evaluation;
  • Multi-Dimensional SAR Data Processing;
  • Interferometric/tomography SAR technique and application in 3D mapping;
  • Fusion and Interpretation of SAR Data with Other Remote Sensing Data;
  • Other related topics.

Dr. Rui Guo
Dr. Huizhang Yang
Dr. Lianhuan Wei
Prof. Dr. Yuxiao Qin
Dr. Jian Kang
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-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

  • multi-dimensional SAR imaging
  • SAR interpretation
  • SAR target detection/recognition
  • polarimetric SAR
  • Interferometric/tomographic SAR
  • remote sensing data fusion
  • SAR signal/image processing with AI
  • anti-interference of SAR

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

26 pages, 8513 KB  
Article
A Sparsity-Assisted Minimum-Entropy Autofocus Algorithm for SAR Moving Target Imaging
by Xuejiao Wen, Xiaolan Qiu and Weidong Chen
Remote Sens. 2026, 18(3), 529; https://doi.org/10.3390/rs18030529 - 6 Feb 2026
Viewed by 372
Abstract
To address the slow convergence and sensitivity to a low signal-to-noise ratio (SNR) of the minimum-entropy autofocus (MEA) algorithm in the refocusing of moving targets, this paper proposes a sparsity-assisted minimum-entropy autofocus algorithm. Within the framework of the traditional gradient descent MEA with [...] Read more.
To address the slow convergence and sensitivity to a low signal-to-noise ratio (SNR) of the minimum-entropy autofocus (MEA) algorithm in the refocusing of moving targets, this paper proposes a sparsity-assisted minimum-entropy autofocus algorithm. Within the framework of the traditional gradient descent MEA with variable step size, the proposed method introduces soft-thresholding-based sparse reconstruction to make moving targets more prominent and suppress background clutter in the image domain. A joint metric combining image entropy and the Hoyer sparsity measure is then constructed, and a three-point adaptive, variable step-size search is employed to reduce the number of evaluations of the cost function, thereby effectively mitigating clutter interference and significantly accelerating the optimization while maintaining good focusing quality. Simulation and real-data experiments demonstrate that, under complex phase errors and different SNR conditions, the proposed algorithm outperforms the conventional variable-step MEA in terms of image entropy, image sparsity, and runtime, while keeping the phase error estimation accuracy within a small range. These results indicate that the proposed method can achieve satisfactory moving-target focusing performance and exhibits promising engineering applicability. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Imaging and Signal Processing Technologies)
►▼ Show Figures

Graphical abstract

30 pages, 25344 KB  
Article
PTU-Net: A Polarization-Temporal U-Net for Multi-Temporal Sentinel-1 SAR Crop Classification
by Feng Tan, Xikai Fu, Huiming Chai and Xiaolei Lv
Remote Sens. 2026, 18(3), 514; https://doi.org/10.3390/rs18030514 - 5 Feb 2026
Viewed by 285
Abstract
Accurate crop type mapping remains challenging in regions where persistent cloud cover limits the availability of optical imagery. Multi-temporal dual-polarization Sentinel-1 SAR data offer an all-weather alternative, yet existing approaches often underutilize polarization information and rely on single-scale temporal aggregation. This study proposes [...] Read more.
Accurate crop type mapping remains challenging in regions where persistent cloud cover limits the availability of optical imagery. Multi-temporal dual-polarization Sentinel-1 SAR data offer an all-weather alternative, yet existing approaches often underutilize polarization information and rely on single-scale temporal aggregation. This study proposes PTU-Net, a polarization–temporal U-Net designed specifically for pixel-wise crop segmentation from SAR time series. The model introduces a Polarization Channel Attention module to construct physically meaningful VV/VH combinations and adaptively enhance their contributions. It also incorporates a Multi-Scale Temporal Self-Attention mechanism to model pixel-level backscatter trajectories across multiple spatial resolutions. Using a 12-date Sentinel-1 stack over Kings County, California, and high-quality crop-type reference labels, the model was trained and evaluated under a spatially independent split. Results show that PTU-Net outperforms GRU, ConvLSTM, 3D U-Net, and U-Net–ConvLSTM baselines, achieving the highest overall accuracy and mean IoU among all tested models. Ablation studies confirm that both polarization enhancement and multi-scale temporal modeling contribute substantially to performance gains. These findings demonstrate that integrating polarization-aware feature construction with scale-adaptive temporal reasoning can substantially improve the effectiveness of SAR-based crop mapping, offering a promising direction for operational agricultural monitoring. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Imaging and Signal Processing Technologies)
►▼ Show Figures

Graphical abstract

26 pages, 10014 KB  
Article
Dynamic Monitoring and Analysis of Mountain Excavation and Land Creation Projects in Lanzhou Using Multi-Source Remote Sensing and Machine Learning
by Quanfu Niu, Jiaojiao Lei, Qiong Fang and Lifeng Zhang
Remote Sens. 2026, 18(2), 273; https://doi.org/10.3390/rs18020273 - 14 Jan 2026
Viewed by 434
Abstract
Mountain Excavation and Land Creation Projects (MELCPs) have emerged as a critical strategy for expanding urban development space in mountainous regions facing land scarcity. Dynamic monitoring and risk management of these projects are essential for promoting sustainable urban development. This study develops an [...] Read more.
Mountain Excavation and Land Creation Projects (MELCPs) have emerged as a critical strategy for expanding urban development space in mountainous regions facing land scarcity. Dynamic monitoring and risk management of these projects are essential for promoting sustainable urban development. This study develops an integrated monitoring framework for MELCPs by combining ascending and descending Sentinel-1 SAR data, Sentinel-2 optical imagery, SRTM digital elevation models (DEM), and field survey data. The framework incorporates multi-temporal change detection, random forest classification, and time-series InSAR analysis to systematically capture the spatiotemporal evolution and subsidence mechanisms associated with MELCPs. Key findings include: (1) The use of dual-orbit SAR data significantly improves the detection accuracy of excavation areas, achieving an overall accuracy of 87.1% (Kappa = 0.85) and effectively overcoming observation limitations imposed by complex terrain. (2) By optimizing the combination of spectral, texture, topographic, and polarimetric features using a random forest algorithm, the classification accuracy of MELCPs is enhanced to 91.2% (Kappa = 0.889). This enables precise annual identification of MELCP progression from 2017 to 2022, revealing a three-stage evolution pattern: concentrated expansion, peak activity, and restricted slowdown. Specifically, the reclaimed area increased from 2.66 km2 (pre-2018) to a peak of 12.61 km2 in 2021, accounting for 34.56% of the total area of the study region, before decreasing to 2.69 km2 in 2022. (3) InSAR monitoring from 2017 to 2023 indicates that areas with only filling experience minor shallow subsidence (<50 mm), whereas subsequent building loads and underground engineering activities lead to continuous deep soil consolidation, with maximum cumulative subsidence reaching 333.8 mm. This study demonstrates that subsidence in MELCPs follows distinct spatiotemporal patterns and is predictable, offering important theoretical insights and practical tools for engineering safety management and territorial spatial optimization in mountainous cities. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Imaging and Signal Processing Technologies)
►▼ Show Figures

Figure 1

29 pages, 2164 KB  
Article
Electromagnetic Scattering Characteristic-Enhanced Dual-Branch Network with Simulated Image Guidance for SAR Ship Classification
by Yanlin Feng, Xikai Fu, Shangchen Feng, Xiaolei Lv and Yiyi Wang
Remote Sens. 2026, 18(2), 252; https://doi.org/10.3390/rs18020252 - 13 Jan 2026
Viewed by 383
Abstract
Synthetic aperture radar (SAR), with its unique imaging principle and technical characteristics, has significant advantages in surface observation and thus has been widely applied in tasks such as object detection and target classification. However, limited by the lack of labeled SAR image datasets, [...] Read more.
Synthetic aperture radar (SAR), with its unique imaging principle and technical characteristics, has significant advantages in surface observation and thus has been widely applied in tasks such as object detection and target classification. However, limited by the lack of labeled SAR image datasets, the accuracy and generalization ability of the existing models in practical applications still need to be improved. In order to solve this problem, this paper proposes a spaceborne SAR image simulation technology and innovatively introduces the concept of bounce number map (BNM), establishing a high-resolution, parameterized simulated data support system for target recognition and classification tasks. In addition, an electromagnetic scattering characteristic-enhanced dual-branch network with simulated image guidance for SAR ship classification (SeDSG) was designed in this paper. It adopts a multi-source data utilization strategy, taking SAR images as the main branch input to capture the global features of real scenes, and using simulated data as the auxiliary branch input to excavate the electromagnetic scattering characteristics and detailed structural features. Through feature fusion, the advantages of the two branches are integrated to improve the adaptability and stability of the model to complex scenes. Experimental results show that the classification accuracy of the proposed network is improved on the OpenSARShip and FUSAR-Ship datasets. Meanwhile, the transfer learning classification results based on the SRSDD dataset verify the enhanced generalization and adaptive capabilities of the network, providing a new approach for data classification tasks with an insufficient number of samples. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Imaging and Signal Processing Technologies)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop