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Spaceborne High-Resolution SAR Imaging

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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: closed (31 March 2024) | Viewed by 11734

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Dr. Jianlai Chen

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School of Aeronautics and Astronautics, Central South University, Changsha 410083, China
Interests: synthetic aperture radar (SAR) imaging; radar image recognition and interpretation
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Dr. Yi Xiong

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College of Automation, Central South University, Changsha 410083, China
Interests: nonlinear SAR imaging

Prof. Dr. Hanwen Yu

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School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: InSAR signal processing and application; phase unwrapping; algorithm design; machine learning
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Prof. Dr. Jian Peng

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Helmholtz Centre for Environmental Research—UFZ, Permoserstraße 15, 04318 Leipzig, Germany
Interests: quantitative retrieval of land surface parameters from remote sensing data; radiative transfer in soil–vegetation–atmosphere systems; process-based modeling and data-driven methods; hydroclimatic extremes
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Prof. Dr. Mengdao Xing

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National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: synthetic aperture radar (SAR); inverse SAR; sparse signal processing
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Dr. Yang Lan

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School of Mechano-Electronic Engineering, Xidian University, Xi’an 710017, China
Interests: phase unwrapping and interferometric synthetic aperture radar signal processing

Special Issue Information

Dear Colleagues,

Spaceborne SAR is a wide-range active microwave imaging equipment, which has great applicative value in military reconnaissance, topographic mapping, disaster monitoring, agricultural and forestry detection and other related areas. As the wide application of spaceborne synthetic aperture radar continues to progress, the demand for high spatial resolution and high temporal resolution in both military reconnaissance and civilian monitoring applications is increasing. At present, the resolution of the most advanced microwave photonic radar has reached the centimeter or even the millimeter level; however, this also poses a number of challenges to the design and imaging of the SAR system, such as the contradiction between a high resolution and a wide swath, the bending problem of satellite orbits, poor real-time imaging, and difficulties involved in image recognition caused by big data. In view of the above problems, the innovative development of new systems and technology for spaceborne high-resolution SAR will become the focus of research.

The focus of this Special Issue is to report the latest high-resolution spaceborne SAR imaging systems and technology. Specifically, it welcomes topics that include (but are not limited to) the research of advanced radar technology, the latest spaceborne SAR imaging theory, target detection and recognition based on spaceborne SAR images, and the acquisition and mining of image target information.

Novel spaceborne SAR missions, system, technique.
HRWS SAR
SAR satellite networking
Moving target detection and focusing with long synthetic aperture time
Spaceborne SAR interference
Three-dimensional deformation measurement of spaceborne SAR
Microwave photonic SAR imaging and its applications

Dr. Jianlai Chen
Dr. Yi Xiong
Prof. Dr. Hanwen Yu
Prof. Dr. Jian Peng
Prof. Dr. Mengdao Xing
Dr. Yang Lan
Guest Editors

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Keywords

new imaging mechanism and theory
high-resolution imaging
advance Radar techniques
multi-source data fusion
target detection

Published Papers (12 papers)

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Research

25 pages, 5011 KiB

Open AccessArticle

A Sparse SAR Imaging Method for Low-Oversampled Staggered Mode via Compound Regularization

by Mingqian Liu, Jie Pan, Jinbiao Zhu, Zhengchao Chen, Bingchen Zhang and Yirong Wu

Remote Sens. 2024, 16(8), 1459; https://doi.org/10.3390/rs16081459 - 20 Apr 2024

Viewed by 200

Abstract

High-resolution wide-swath (HRWS) imaging is the research focus of the modern spaceborne synthetic-aperture radar (SAR) imaging field, with significant relevance and vast application potential. Staggered SAR, as an innovative imaging system, mitigates blind areas across the entire swath by periodically altering the radar pulse repetition interval (PRI), thereby extending the swath width to multiples of that achievable by conventional systems. However, the staggered mode introduces inherent challenges, such as nonuniform azimuth sampling and echo data loss, leading to azimuth ambiguities and substantially impacting image quality. This paper proposes a sparse SAR imaging method for the low-oversampled staggered mode via compound regularization. The proposed method not only effectively suppresses azimuth ambiguities arising from nonuniform sampling without necessitating the restoration of missing echo data, but also incorporates total variation (TV) regularization into the sparse reconstruction model. This enhances the accurate reconstruction of distributed targets within the scene. The efficacy of the proposed method is substantiated through simulations and real data experiments from spaceborne missions. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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15 pages, 9118 KiB

Open AccessArticle

Miniaturization Design of High-Integration Unmanned Aerial Vehicle-Borne Video Synthetic Aperture Radar Real-Time Imaging Processing Component

by Tao Yang, Tong Wang, Nannan Zheng, Shuangxi Zhang, Fanteng Meng, Xinyu Zhang and Qirui Wu

Remote Sens. 2024, 16(7), 1273; https://doi.org/10.3390/rs16071273 - 04 Apr 2024

Viewed by 406

Abstract

The unmanned aerial vehicle (UAV)-borne video synthetic aperture radar (SAR) possesses the characteristic of having high-continuous-frame-rate imaging, which is conducive to the real-time monitoring of ground-moving targets. The real-time imaging-processing system for UAV-borne video SAR (ViSAR) requires miniaturization, low power consumption, high frame rate, and high-resolution imaging. In order to achieve high-frame-rate real-time imaging on limited payload-carrying platforms, this study proposes a miniaturization design of a high-integration UAV-borne ViSAR real-time imaging-processing component (MRIPC). The proposed design integrates functions such as broadband signal generation, high-speed real-time sampling, and real-time SAR imaging processing on a single-chip FPGA. The parallel access mechanism using multiple sets of high-speed data buffers increases the data access throughput and solves the problem of data access bandwidth. The range-Doppler (RD) algorithm and map-drift (MD) algorithm are optimized using parallel multiplexing, achieving a balance between computing speed and hardware resources. The test results have verified that our proposed component is effective for the real-time processing of 2048 × 2048 single-precision floating-point data points to realize a 5 Hz imaging frame rate and 0.15 m imaging resolution, satisfying the requirements of real-time ViSAR-imaging processing. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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19 pages, 11357 KiB

Open AccessArticle

Two-Step Accuracy Improvement for Multitarget Detection in Complex Environment Using UWB Radar

by Zhihuan Liang, Yanghao Jin, Degui Yang, Buge Liang and Jinjun Mo

Remote Sens. 2024, 16(5), 877; https://doi.org/10.3390/rs16050877 - 01 Mar 2024

Viewed by 548

Abstract

Detecting multiple human targets in indoor scenarios using ultra-wideband (UWB) radar usually involves false detection results caused by the secondary reflections, which might reduce the target detection accuracy and cause a more severe deterioration when the number of targets increases. This article proposed a two-step accuracy improvement method for multitarget detection in environments with multiple human targets of more than three and strong secondary reflections by the surroundings, especially the walls. Based on the rough detection results acquired by the modified CA-CFAR (MCA-CFAR) processing, the first step achieves the primary false alarm suppression using a short-window accumulation in the time domain. Then, the second step applies the decision confidence on the detection results from the first step to assess the reliability of results for improved accuracy. The two-step accuracy improvement could thus have a higher accuracy through cascading false alarm suppression. The effectiveness and accuracy of the proposed algorithm are verified based on the experimental results. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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20 pages, 9488 KiB

Open AccessArticle

PolSAR Image Classification Based on Multi-Modal Contrastive Fully Convolutional Network

by Wenqiang Hua, Yi Wang, Sijia Yang and Xiaomin Jin

Remote Sens. 2024, 16(2), 296; https://doi.org/10.3390/rs16020296 - 11 Jan 2024

Viewed by 858

Abstract

Deep neural networks have achieved remarkable results in the field of polarimetric synthetic aperture radar (PolSAR) image classification. However, PolSAR is affected by speckle imaging, resulting in PolSAR images usually containing a large amount of speckle noise, which usually leads to the poor spatial consistency of classification results and insufficient classification accuracy. Semantic segmentation methods based on deep learning can realize the task of image segmentation and classification at the same time, producing fine-grained and smooth classification maps. However, these approaches require enormous labeled data sets, which are laborious and time-consuming. Due to these issues, a new multi-modal contrastive fully convolutional network, named MCFCN, is proposed for PolSAR image classification in this paper, which combines multi-modal features of the same pixel as inputs to the model based on a fully convolutional network and accomplishes the classification task using only a small amount of labeled data through contrastive learning. In addition, to describe the PolSAR terrain targets more comprehensively and enhance the robustness of the classifier, a pixel overlapping classification strategy is proposed, which can not only improve the classification accuracy effectively but also enhance the stability of the method. The experiments demonstrate that compared with existing classification methods, the classification results of the proposed method for three real PolSAR datasets have higher classification accuracy. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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39 pages, 8103 KiB

Open AccessArticle

Advancements in Spaceborne Synthetic Aperture Radar Imaging with System-on-Chip Architecture and System Fault-Tolerant Technology

by Yu Xie, Yizhuang Xie, Bingyi Li and He Chen

Remote Sens. 2023, 15(19), 4739; https://doi.org/10.3390/rs15194739 - 27 Sep 2023

Cited by 3 | Viewed by 1153

Abstract

With the continuous development of satellite payload and system-on-chip (SoC) technology, spaceborne real-time synthetic aperture radar (SAR) imaging systems play a crucial role in various defense and civilian domains, including Earth remote sensing, military reconnaissance, disaster mitigation, and resource exploration. However, designing high-performance and high-reliability SAR imaging systems that operate in harsh environmental conditions while adhering to strict size, weight, and power consumption constraints remains a significant challenge. In this paper, we introduce a spaceborne SAR imaging chip based on a SoC architecture with system fault-tolerant technology. The fault-tolerant SAR SoC architecture has a CPU, interface subsystem, memory subsystem, data transit subsystem, and data processing subsystem. The data processing subsystem, which includes fast Fourier transform (FFT) modules, coordinated rotation digital computer (CORDIC) modules (for phase factor calculation), and complex multiplication modules, is the most critical component and can achieve various modes of SAR imaging. Through analyzing the computational requirements of various modes of SAR, we found that FFT accounted for over 50% of the total computational workload in SAR imaging processing, while the CORDIC modules for phase factor generation accounted for around 30%. Therefore, ensuring the fault tolerance of these two modules is crucial. To address this issue, we propose a word-length optimization redundancy (WLOR) method to make the fixed-point pipelined FFT processors in FFT modules fault tolerant. Additionally, we propose a fault-tolerant pipeline CORDIC architecture utilizing error correction code (ECC) and sum of squares (SOS) check. For other parts of the SoC architecture, we propose a generic partial triple modular redundancy (TMR) hardening method based on the HITS algorithm to improve fault tolerance. Finally, we developed a fully automated FPGA-based fault injection platform to test the design’s effectiveness by injecting errors at arbitrary locations. The simulation results demonstrate that the proposed methods significantly improved the chip’s fault tolerance, making the SAR imaging chip safer and more reliable. We also implemented a prototype measurement system with a chip-included board and demonstrated the proposed design’s performance on the Chinese Gaofen-3 strip-map continuous imaging system. The chip requires 9.2 s, 50.6 s, and 7.4 s for a strip-map with 16,384 × 16,384 granularity, multi-channel strip-map with 65,536 × 8192 granularity, and multi-channel scan mode with 32,768 × 4096 granularity, respectively, and the system hardware consumes 6.9 W of power to process the SAR raw data. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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13 pages, 4035 KiB

Open AccessCommunication

Feature Enhancement Using Multi-Baseline SAR Interferometry-Correlated Synthesis Images for Power Transmission Tower Detection in Mountain Layover Area

by Baolong Wu, Haonan Wang and Jianlai Chen

Remote Sens. 2023, 15(15), 3823; https://doi.org/10.3390/rs15153823 - 31 Jul 2023

Cited by 1 | Viewed by 794

Abstract

The detection performance of power transmission towers in mountainous areas using SAR amplitude images is obviously influenced by the strong layover background (mainly including vegetation and soil) clutter interference around the towers. In this paper, power transmission tower detection in a mountainous layover area, using single-baseline SAR interferometry coherence images, which show better feature enhancement effectiveness compared to SAR amplitude images, is presented. Moreover, a novel feature enhancement method, that of generating multi-baseline SAR interferometry-correlated synthesis images for power transmission tower detection in a mountain layover area, is proposed. It demonstrates better feature enhancement (layover background cluster suppression) than that using single-baseline SAR interferometry coherence images. Theoretical analysis illustrates that the mountainous layover background clutter interference can be suppressed in the proposed single-baseline/multi-baseline SAR interferometry-correlated synthesis image. Experiments including over 12 repeat-pass TerraSAR-X staring spotlight mode acquisitions were conducted, and the results demonstrate that the detection performance with the use of multi-baseline SAR interferometry-correlated synthesis images showed an improvement of more than 43.6%, compared with the traditional method of using SAR amplitude images when benchmark deep learning-based detectors are used, i.e., Faster RCNN and YOLOv7. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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12 pages, 1085 KiB

Open AccessCommunication

Accuracy Improvement of High-Resolution Wide-Swath Spaceborne Synthetic Aperture Radar Imaging with Low Pule Repetition Frequency

by Xiaofeng Wang, Yaduan Ruan and Xinggan Zhang

Remote Sens. 2023, 15(15), 3811; https://doi.org/10.3390/rs15153811 - 31 Jul 2023

Viewed by 641

Abstract

For a single-channel spaceborne synthetic aperture radar (SAR), the usage of a low pulse repetition frequency (PRF) is an effective technical way to extend the range swath. The sub-aperture imaging strategy is usually used to solve the problem of azimuth spectrum aliasing under the condition of a low PRF. However, the required up-sampling processing before the coherent synthesis of sub-images will lead to spectrum discontinuity between adjacent sub-images, which leads to an obvious grating lobe phenomenon after the process of sub-image synthesis, resulting in a significant decrease in image quality. For this issue, a high-resolution wide-swath (HRWS) imaging algorithm for a spaceborne SAR with a low PRF is proposed in this paper based on optimal spectrum shift processing. First, each sub-aperture is imaged using the typical range migration algorithm (RMA), and then all sub-images are up-sampled at the same time. Then, based on the criterion of the minimum grating lobe, the optimal spectrum shift is estimated. Finally, the spectrum of all sub-images is shifted and then all the shifted sub-images are synthesized coherently. The simulation data processing results verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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26 pages, 13278 KiB

Open AccessArticle

Fast and Accurate Refocusing for Moving Ships in SAR Imagery Based on FrFT

by Jin Wang, Xiangguang Leng, Zhongzhen Sun, Xi Zhang and Kefeng Ji

Remote Sens. 2023, 15(14), 3656; https://doi.org/10.3390/rs15143656 - 21 Jul 2023

Cited by 2 | Viewed by 1097

Abstract

Synthetic aperture radar (SAR) is capable of monitoring the ocean all day, regardless of weather conditions. However, moving ships exhibit azimuth defocus in SAR images, which severely hampers ship recognition performance. Ships typically move in a linear motion at sea. For refocusing linear moving ships, existing SAR autofocus algorithms cannot accurately extract defocus information and require multiple iterations. To overcome the poor focusing quality and high computational complexity of existing refocusing algorithms, this paper proposes a fast and accurate refocusing scheme for moving ships in SAR imagery based on Fractional Fourier Transform (FrFT). Firstly, the azimuth line with the strongest energy in the SAR image is selected as the best azimuth line representing its motion property. Then, according to the entropy variation law of the azimuth line after FrFT, the azimuth line’s optimal rotation order is determined by the proposed minimum entropy search method, which can accurately and quickly obtain defocus information. In the final refocusing module, the scheme provides two ways, i.e., fast or fine refocusing approaches, to generate well-focused images. The fast refocusing approach performs FrFT on each azimuth line at the optimal rotation order of the best azimuth line. The fine refocusing approach takes the optimal rotation order of the best azimuth line as the initial value and further searches for the optimal rotation order of other azimuth lines. In order to verify the effectiveness of the proposed scheme, experiments are carried out on a number of Gaofen-3 SAR images in different acquisition modes. The experimental results show that the proposed fast refocusing approach can achieve the fastest speed, which is 2.1% of the traditional FrFT-based method’s processing time. Moreover, the proposed fine refocusing approach has the best focusing performance, achieving the lowest image entropy among existing methods. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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19 pages, 4224 KiB

Open AccessArticle

DAFCNN: A Dual-Channel Feature Extraction and Attention Feature Fusion Convolution Neural Network for SAR Image and MS Image Fusion

by Jiahao Luo, Fang Zhou, Jun Yang and Mengdao Xing

Remote Sens. 2023, 15(12), 3091; https://doi.org/10.3390/rs15123091 - 13 Jun 2023

Cited by 1 | Viewed by 1268

Abstract

In the field of image fusion, spatial detail blurring and color distortion appear in synthetic aperture radar (SAR) images and multispectral (MS) during the traditional fusion process due to the difference in sensor imaging mechanisms. To solve this problem, this paper proposes a fusion method for SAR images and MS images based on a convolutional neural network. In order to make use of the spatial information and different scale feature information of high-resolution SAR image, a dual-channel feature extraction module is constructed to obtain a SAR image feature map. In addition, different from the common direct addition strategy, an attention-based feature fusion module is designed to achieve spectral fidelity of the fused images. In order to obtain better spectral and spatial retention ability of the network, an unsupervised joint loss function is designed to train the network. In this paper, the Sentinel 1 SAR images and Landsat 8 MS images are used as datasets for experiments. The experimental results show that the proposed algorithm has better performance in quantitative and visual representation when compared with traditional fusion methods and deep learning algorithms. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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17 pages, 3667 KiB

Open AccessArticle

A Two-Stage Aerial Target Localization Method Using Time-Difference-of-Arrival Measurements with the Minimum Number of Radars

by Jinming Chen, Yu Li, Xiaochao Yang, Qi Li, Fei Liu, Weiwei Wang, Caipin Li and Chongdi Duan

Remote Sens. 2023, 15(11), 2829; https://doi.org/10.3390/rs15112829 - 29 May 2023

Cited by 1 | Viewed by 1005

Abstract

Distributed radar systems promise to significantly enhance target localization by virtue of the superiority of multi-view observations from widely separated radars, compared to their monostatic counterparts. Nevertheless, when the radar number is limited, performing target localization bears the brunt of the parameter identifiability requirement that the parameter number must be no less than the number of independent measurements. In this way, the canonical two-stage target localization method, as well as its developments, is no longer appropriate for direct application. Hence, in this paper, we propose a novel target localization method using time-difference-of-arrival (TDOA) measurements with the minimum number of radars under platform position uncertainties. The referred distributed system is a bistatic multi-receiver system, where the primary signal is transmitted by a geostationary Earth orbit (GEO) satellite while receivers are equipped on several unmanned aerial vehicles (UAVs). In the first stage, the reference range from the reference radar to the target is estimated by a quadratic function, and then the weighted least squares (WLS) solution of the target location is updated by substituting the range estimate back into it. In the second stage, we invoke the Taylor series approximation to further refine the target localization obtained by the first stage. It can be foreseen that the developed method is beneficial for scenarios with a limited number of radars, including engineering projects such as fire control, surveillance, and guidance, to support high-accuracy target localization. The simulation results show the superiority of the localization performance of the proposed method over other existing methods. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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26 pages, 48502 KiB

Open AccessArticle

Hybrid Domain Efficient Modulation-Based Deceptive Jamming Algorithm for Nonlinear-Trajectory Synthetic Aperture Radar

by Jiaming Dong, Qunying Zhang, Wei Lu, Wenhai Cheng and Xiaojun Liu

Remote Sens. 2023, 15(9), 2446; https://doi.org/10.3390/rs15092446 - 06 May 2023

Cited by 1 | Viewed by 1290

Abstract

Deception jamming of synthetic aperture radar (SAR) has attracted extensive attention due to its low power consumption and high fidelity advantages. However, existing SAR deception jamming algorithms assume that SAR operates on a linear trajectory. In practice, SAR trajectories often become nonlinear due to factors such as atmospheric turbulence, which results in the jamming signals lacking the two-dimensional spatial variability of nonlinear-trajectory SAR echo signal and affects the imaging quality of deception jamming. This paper proposes a new algorithm for nonlinear-trajectory airborne SAR deception jamming based on hybrid domain efficient (HDE) modulation. This algorithm derives the jamming frequency response (JFR) with SAR trajectory deviation in the azimuth time–frequency hybrid domain. Based on the hybrid domain modulation, the jammer calculates the JFR of the linear trajectory in the azimuth frequency domain and constructs for the real-time trajectory deviation pulse by pulse at each azimuth moment. The real-time modulation process of the algorithm only involves range domain Fourier transform and complex multiplication, combining computational efficiency and modulation flexibility. The validity constraints of the algorithm have been analyzed to ensure the focusing ability of the jamming signal. Simulation and computational complexity analysis validate the excellent performance of the algorithm in imaging quality and efficiency. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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21 pages, 13966 KiB

Open AccessArticle

PolSAR Image Classification Based on Relation Network with SWANet

by Wenqiang Hua, Yurong Zhang, Cong Zhang and Xiaomin Jin

Remote Sens. 2023, 15(8), 2025; https://doi.org/10.3390/rs15082025 - 11 Apr 2023

Cited by 2 | Viewed by 1453

Abstract

Deep learning and convolutional neural networks (CNN) have been widely applied in polarimetric synthetic aperture radar (PolSAR) image classification, and satisfactory results have been obtained. However, there is one crucial issue that still has not been solved. These methods require abundant labeled samples and obtaining the labeled samples of PolSAR images is usually time-consuming and labor-intensive. To obtain better classification results with fewer labeled samples, a new attention-based 3D residual relation network (3D-ARRN) is proposed for PolSAR image. Firstly, a multilayer CNN with residual structure is used to extract depth polarimetric features. Secondly, to extract more important feature information and improve the classification results, a spatial weighted attention network (SWANet) is introduced to concentrate the feature information, which is more favorable for a classification task. Then, the features of training and test samples are integrated and CNN is utilized to compute the score of similarity between training and test samples. Finally, the similarity score is used to determine the category of test samples. Studies on four different PolSAR datasets illustrate that the proposed 3D-ARRN model can achieve higher classification results than other comparison methods with few labeled data. Full article

(This article belongs to the Special Issue Spaceborne High-Resolution SAR Imaging)

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