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Remote Sensing
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Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing
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Advances in Synthetic Aperture Radar (SAR) Signal and 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: closed (26 April 2024) | Viewed by 2391

Special Issue Editors

Dr. Zhongyu Li

E-Mail Website
Guest Editor
The School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: SAR target detection and imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Hongyang An

E-Mail Website
Guest Editor
The School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: SAR imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Yan Wang

E-Mail Website
Guest Editor
Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: SAR system and imaging
Prof. Dr. Shiyang Tang

E-Mail Website
Guest Editor
National Laboratory of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: synthetic aperture radar (SAR); ground moving target indication (GMTI); radar imaging; real-time SAR processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alin Achim

E-Mail Website
Guest Editor
Department of Electrical & Electronic Engineering, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol BS8 1UB, UK
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), as an active microwave imaging system, has advanced rapidly since its birth in the 1950s and has been increasingly applied in the domains such as environmental and Earth monitoring, climate change, ocean resource utilization, battlefield perception and reconnaissance. In recent years, with the development of numerous types of electronic technologies, such as Terahertz technology and Microwave photonics technology, SAR systems will have a higher range and azimuth bandwidth, with the potential for ultra-high-resolution imaging, which can greatly improve the accuracy of remote sensing data. On the other hand, with the increasing maturity of platform technologies such as small satellites and drones, multiple SAR payloads can be placed on these platforms to form a distributed or clustered SAR system, which has the ability to obtain multi-dimensional scattering information from different perspectives, frequencies, polarization, and other aspects of the observation area. This will greatly enrich the means of obtaining microwave remote sensing data. However, complex target scattering characteristics, non-ideal motion and synchronization, and high-dimensional data pose significant challenges to SAR high-resolution imaging and the multi-dimensional fusion process. Along with the appearance of new challenges and processing techniques, multiple research issues remain with regard to SAR signal and image processing, such as the modeling of scattering characteristics, error estimation and compensation, target component information extraction and information fusion, and the combination of artistic intelligence techniques, among others. This Special Issue aims to collect and highlight outstanding contributions that cover “Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing”, including (but not limited to) the following:

  • SAR target scattering characteristic analysis.
  • SAR motion error estimation and compensation.
  • The multistatic SAR synchronization method.
  • SAR high-resolution imaging.
  • 3D SAR imaging.
  • Video SAR imaging.
  • SAR target information extraction and fusion.
  • Combination of artificial intelligence techniques.

Dr. Zhongyu Li
Dr. Hongyang An
Dr. Yan Wang
Prof. Dr. Shiyang Tang
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
  • error estimation and compensation
  • 3-D SAR imaging
  • video SAR imaging
  • information extraction
  • information fusion
  • artificial intelligence

Published Papers (4 papers)

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Research

22 pages, 22810 KiB  
Article
Maritime Moving Target Reconstruction via MBLCFD in Staggered SAR System
by Xin Qi, Yun Zhang, Yicheng Jiang, Zitao Liu, Xinyue Ma and Xuan Liu
Remote Sens. 2024, 16(9), 1550; https://doi.org/10.3390/rs16091550 - 26 Apr 2024
Viewed by 130
Abstract
Imaging maritime targets requires a high resolution and wide swath (HWRS) in a synthetic aperture radar (SAR). When operated with a variable pulse repetition interval (PRI), a staggered SAR can realize HRWS imaging, which needs to be reconstructed due to echo pulse loss [...] Read more.
Imaging maritime targets requires a high resolution and wide swath (HWRS) in a synthetic aperture radar (SAR). When operated with a variable pulse repetition interval (PRI), a staggered SAR can realize HRWS imaging, which needs to be reconstructed due to echo pulse loss and a nonuniformly sampled signal along the azimuth. The existing reconstruction algorithms are designed for stationary scenes in a staggered SAR mode, and thus, produce evident image defocusing caused by complex target motion for moving targets. Typically, the nonuniform sampling and complex motion of maritime targets aggravate the spectrum aliasing in a staggered SAR mode, causing inevitable ambiguity and degradation in its reconstruction performance. To this end, this study analyzed the spectrum of maritime targets in a staggered SAR system through theoretical derivation. After this, a reconstruction method named MBLCFD (Modified Best Linear Unbaised and Complex-Lag Time-Frequency Distribution) is proposed to refocus the blurred maritime target. First, the signal model of the maritime target with 3D rotation accompanying roll–pitch–yaw movement was established under the curved orbit of the satellite. The best linear unbiased (BLU) method was modified to alleviate the coupling of nonuniform sampling and target motion. A precise SAR algorithm was performed based on the method of inverse reversion to counteract the effect of a curved orbit and wide swath. Based on the hybrid SAR/ISAR technique, the complex-lag time-frequency distribution was exploited to refocus the maritime target images. Simulations and experiments were carried out to verify the effectiveness of the proposed method, providing precise refocusing performance in staggered mode. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing)
21 pages, 6432 KiB  
Article
A Texture Enhancement Method for Oceanic Internal Wave Synthetic Aperture Radar Images Based on Non-Local Mean Filtering and Texture Layer Enhancement
by Zhenghua Chen, Hongcheng Zeng, Yamin Wang, Wei Yang, Yanan Guan and Wei Liu
Remote Sens. 2024, 16(7), 1172; https://doi.org/10.3390/rs16071172 - 27 Mar 2024
Viewed by 434
Abstract
Synthetic aperture radar (SAR) is an important tool for observing the oceanic internal wave phenomenon. However, owing to the unstable imaging quality of SAR on oceanic internal waves, the texture details of internal wave images are usually unclear, which is not conducive to [...] Read more.
Synthetic aperture radar (SAR) is an important tool for observing the oceanic internal wave phenomenon. However, owing to the unstable imaging quality of SAR on oceanic internal waves, the texture details of internal wave images are usually unclear, which is not conducive to the subsequent applications of the images. To cope with this problem, a texture enhancement method for oceanic internal wave SAR images is proposed in this paper, which is based on non-local mean (NLM) filtering and texture layer enhancement (TLE). Since the strong speckle noise commonly present in internal wave images is simultaneously enhanced during texture enhancement, resulting in degraded image quality, NLM filtering is first performed to suppress speckle noise. Then, the denoised image is decomposed into the structure layer and the texture layer, and a texture layer enhancement method oriented to the texture characteristics of oceanic internal waves is proposed and applied. Finally, the enhanced texture layer and the structure layer are combined to reconstruct the final enhanced image. Experiments are conducted based on the Gaofen-3 real SAR data, and the results demonstrate that the proposed method performs well in suppressing speckle noise, maintaining overall image brightness, and enhancing internal wave texture details. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing)
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23 pages, 3139 KiB  
Article
Multi-Level Feature-Refinement Anchor-Free Framework with Consistent Label-Assignment Mechanism for Ship Detection in SAR Imagery
by Yun Zhou, Sensen Wang, Haohao Ren, Junyi Hu, Lin Zou and Xuegang Wang
Remote Sens. 2024, 16(6), 975; https://doi.org/10.3390/rs16060975 - 10 Mar 2024
Viewed by 612
Abstract
Deep learning-based ship-detection methods have recently achieved impressive results in the synthetic aperture radar (SAR) community. However, numerous challenging issues affecting ship detection, such as multi-scale characteristics of the ship, clutter interference, and densely arranged ships in complex inshore, have not been well [...] Read more.
Deep learning-based ship-detection methods have recently achieved impressive results in the synthetic aperture radar (SAR) community. However, numerous challenging issues affecting ship detection, such as multi-scale characteristics of the ship, clutter interference, and densely arranged ships in complex inshore, have not been well solved so far. Therefore, this article puts forward a novel SAR ship-detection method called multi-level feature-refinement anchor-free framework with a consistent label-assignment mechanism, which is capable of boosting ship-detection performance in complex scenes. First, considering that SAR ship detection is susceptible to complex background interference, we develop a stepwise feature-refinement backbone network to refine the position and contour of the ship object. Next, we devise an adjacent feature-refined pyramid network following the backbone network. The adjacent feature-refined pyramid network consists of the sub-pixel sampling-based adjacent feature-fusion sub-module and adjacent feature-localization enhancement sub-module, which can improve the detection capability of multi-scale objects by mitigating multi-scale high-level semantic loss and enhancing low-level localization features. Finally, to solve the problems of unbalanced positive and negative samples and densely arranged ship detection, we propose a consistent label-assignment mechanism based on consistent feature scale constraints to assign more appropriate and consistent labels to samples. Extensive qualitative and quantitative experiments on three public datasets, i.e., SAR Ship-Detection Dataset (SSDD), High-Resolution SAR Image Dataset (HRSID), and SAR-Ship-Dataset illustrate that the proposed method is superior to many state-of-the-art SAR ship-detection methods. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing)
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25 pages, 6206 KiB  
Article
A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception
by Tianjiao Zeng, Wensi Zhang, Xu Zhan, Xiaowo Xu, Ziyang Liu, Baoyou Wang and Xiaoling Zhang
Remote Sens. 2024, 16(6), 952; https://doi.org/10.3390/rs16060952 - 08 Mar 2024
Viewed by 575
Abstract
This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and [...] Read more.
This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and multipath interference, shape distortion from high sidelobes, and lack of color and texture information, all of which impede effective target recognition and scattering diagnosis. The proposed approach presents the first known application of multimodal fusion in near-field 3D SAR imaging, integrating LiDAR and optical camera data to overcome its inherent limitations. The framework comprises data preprocessing, point cloud registration, and data fusion, where registration between multi-sensor data is the core of effective integration. Recognizing the inadequacy of traditional registration methods in handling varying data formats, noise, and resolution differences, particularly between near-field 3D SAR and other sensors, this work introduces a novel three-stage registration process to effectively address these challenges. First, the approach designs a structure–intensity-constrained centroid distance detector, enabling key point extraction that reduces heterogeneity and accelerates the process. Second, a sample consensus initial alignment algorithm with SHOT features and geometric relationship constraints is proposed for enhanced coarse registration. Finally, the fine registration phase employs adaptive thresholding in the iterative closest point algorithm for precise and efficient data alignment. Both visual and quantitative analyses of measured data demonstrate the effectiveness of our method. The experimental results show significant improvements in registration accuracy and efficiency, laying the groundwork for future multimodal fusion advancements in near-field 3D SAR imaging. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing)
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