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Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing

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

Deadline for manuscript submissions: 29 November 2024 | Viewed by 10813

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Special Issue Editors

Dr. Zhongyu Li

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The School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: SAR target detection and imaging
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Dr. Hongyang An

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The School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: SAR imaging
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Dr. Yan Wang

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Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: radar system; SAR imaging technology; artificial intelligence and pattern recognition
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Prof. Dr. Shiyang Tang

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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
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Prof. Dr. Alin Achim

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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
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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

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Keywords

SAR image processing
error estimation and compensation
3-D SAR imaging
video SAR imaging
information extraction
information fusion
artificial intelligence

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Published Papers (9 papers)

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23 pages, 10891 KiB

Open AccessArticle

LSR-Det: A Lightweight Detector for Ship Detection in SAR Images Based on Oriented Bounding Box

by Fanlong Meng, Xiangyang Qi and Huaitao Fan

Remote Sens. 2024, 16(17), 3251; https://doi.org/10.3390/rs16173251 - 2 Sep 2024

Viewed by 960

Abstract

Convolutional neural networks (CNNs) have significantly advanced in recent years in detecting arbitrary-oriented ships in synthetic aperture radar (SAR) images. However, challenges remain with multi-scale target detection and deployment on satellite-based platforms due to the extensive model parameters and high computational complexity. To address these issues, we propose a lightweight method for arbitrary-oriented ship detection in SAR images, named LSR-Det. Specifically, we introduce a lightweight backbone network based on contour guidance, which reduces the number of parameters while maintaining excellent feature extraction capability. Additionally, a lightweight adaptive feature pyramid network is designed to enhance the fusion capability of the ship features across different layers with a low computational cost by incorporating adaptive ship feature fusion modules between the feature layers. To efficiently utilize the fused features, a lightweight rotating detection head is designed, incorporating the idea of sharing the convolutional parameters, thereby improving the network’s ability to detect multi-scale ship targets. The experiments conducted on the SAR ship detection dataset (SSDD) and the rotating ship detection dataset (RSDD-SAR) demonstrate that LSR-Det achieves an average precision (AP₅₀) of 98.5% and 97.2% with 3.21 G floating point operations (FLOPs) and 0.98 M parameters, respectively, outperforming the current popular SAR arbitrary-direction ship target 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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26 pages, 5746 KiB

Open AccessArticle

A Novel SAR Imaging Method for GEO Satellite–Ground Bistatic SAR System with Severe Azimuth Spectrum Aliasing and 2-D Spatial Variability

by Jingjing Ti, Zhiyong Suo, Yi Liang, Bingji Zhao and Jiabao Xi

Remote Sens. 2024, 16(15), 2853; https://doi.org/10.3390/rs16152853 - 3 Aug 2024

Viewed by 1013

Abstract

The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing)

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17 pages, 4414 KiB

Open AccessArticle

Spaceborne HRWS-SAR-GMTI System Design Method with Optimal Configuration

by Yan Jiang, Lingyu Wang, Qing Ling, Jingtao Ma, Penghui Huang, Xingzhao Liu and Jixia Fan

Remote Sens. 2024, 16(12), 2148; https://doi.org/10.3390/rs16122148 - 13 Jun 2024

Cited by 1 | Viewed by 730

Abstract

The spaceborne high-resolution and wide-swath synthetic aperture radar (HRWS-SAR) system combined with the ground moving target indication (GMTI) mode provides a promising prospect in the realization of wide-area target surveying and high-resolution target imaging. In this paper, a system design method is proposed for an HRWS-SAR-GMTI system with ideal reconstruction configuration. In the proposed method, the whole azimuth receiving channels are uniformly divided into multiple groups, where HRWS-SAR imaging is implemented in each sub-group and then GMTI processing is performed based on the reconstructed SAR images. Then, an optimal candidate PRF is properly selected with respect to the optimal reconstruction configuration. After that, the digital beam forming scanning on receive (DBF-SCORE) technique is applied to further enlarge the range swath and improve the noise equivalent scattering coefficient (NESZ). Based on the predesigned system, HRWS-SAR image-based GMTI processing can finally be accomplished. The effectiveness of the proposed method is validated by simulated experiments. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing)

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22 pages, 1843 KiB

Open AccessArticle

Long-Time Coherent Integration for the Spatial-Based Bistatic Radar Based on Dual-Scale Decomposition and Conditioned CPF

by Suqi Li, Yihan Wang, Yanfeng Liang and Bailu Wang

Remote Sens. 2024, 16(10), 1798; https://doi.org/10.3390/rs16101798 - 18 May 2024

Viewed by 1041

Abstract

This paper addresses the problem of weak maneuvering target detection in the space-based bistatic radar system through long-time coherent integration (LTCI). The space-based bistatic radar is vulnerable to the high-order range migration (RM) and Doppler frequency migration (DFM), since the target, the receiver and the transmitter all can play fast movement independently. To correct high- order RM and DFM, this usually involves joint high-dimensional parameter searching, incurring a large computational burden. In our previous work, a dual-scale (DS) decomposition of motion parameters was proposed, in which the optimal GRFT is conditionally decoupled into two cascade procedures called the modified generalized inverse Fourier transform (GIFT) and generalized Fourier transform (GFT), resulting in the DS-GRFT detector. However, even if the DS-GRFT detector preserves the superior performance and dramatically decreases the complexity, high-dimensional searching is still required. In this paper, by analyzing the structure of the DS-GRFT detector, we further designed a conditioned cubic phase function (CCPF) tailored to the range–slow-time signal after GIFT, breaking the joint high-dimensional searching into independent one-dimensional searching. Then, by connecting the proposed CCPF with the GIFT, we achieved a new LTCI detector called the DS-GIFT-CCPF detector, which obtained a significant computational cost reduction with acceptable performance loss, as demonstrated in numerical experiments. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing)

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22 pages, 22814 KiB

Open AccessArticle

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 773

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 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)

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21 pages, 6432 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1048

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 1133

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 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

Open AccessArticle

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 - 8 Mar 2024

Viewed by 1679

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 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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18 pages, 21702 KiB

Open AccessTechnical Note

Ship Wake Detection in a Single SAR Image via a Modified Low-Rank Constraint

by Yanan Guan, Huaping Xu, Wei Li and Chunsheng Li

Remote Sens. 2024, 16(18), 3487; https://doi.org/10.3390/rs16183487 - 20 Sep 2024

Viewed by 504

Abstract

Ship wake detection stands as a pivotal task in marine environment monitoring. The main challenge in ship wake detection is to improve detection accuracy and mitigate false alarms. To address this challenge, a novel procedure for ship wake detection in a single SAR image is proposed in this study. Initially, an entropy distance similarity criterion is designed to measure nonlocal image patch similarity. Based on the proposed criterion, a low-rank and sparse decomposition method is modified using nonlocal similar patch matrix construction to separate the sparse wake. Subsequently, a field-of-experts (FOE) model is introduced to generate a series of multi-view wake feature maps, which are fused to construct an enhanced feature map. The sparse wake is further enhanced in the Radon domain with the enhanced feature map. The experimental results demonstrate the effectiveness of the proposed method on real SAR ship wake images. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Signal and Image Processing)

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