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Distributed Spaceborne SAR: Systems, Algorithms, and Applications

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

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 15635

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Dr. Xichao Dong

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School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: synthetic aperture radar; weather radar; radar signal processing
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Dr. Yuanhao Li

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School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: synthetic aperture radar; interferometric SAR (InSAR); radar altimeter; atmosphere sensing; remote sensing
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Prof. Dr. Cheng Hu

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School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: geosynchronous SAR; SAR signal processing
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Prof. Dr. Andrea Monti Guarnieri

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Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy
Interests: SAR; radar Interferometry; geosynchronous SAR; MIMO radar; radar constellations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of satellite technology, the size of entire satellites is decreasing, and the cost is significantly reduced; therefore, distributed spaceborne synthetic aperture radar (SAR) systems are coming into focus. ICEYE and Capella Space have launched several distributed SAR satellites. Moreover, Chinese LuTan-1 Mission, European Space Agency (ESA) Harmony Mission, German Aerospace Center (DLR) TanDEM-L Mission, Netherlands Institute for Space Research (SRON) SwarmSAR Mission, and the distributed geosynchronous SAR (GEO SAR) are also in the preparation and planning stage. Compared with the traditional single-satellite SAR platform, distributed spaceborne SAR systems have the great advantages of a shorter revisit time, larger imaging coverage, and wider remote sensing application scope due to their system flexibility and inter-satellite collaboration. Moreover, the spaceborne-airborne bistatic SAR configuration further extends the SAR’s capability, such as providing more information about scattering properties and achieving forward-looking SAR imaging. Therefore, they can overcome bottlenecks such as temporal decorrelation and atmospheric interference and help to improve performance in topography and deformation retrieval, moving target detection, and three-dimensional (3D) imaging. With the theme of distributed spaceborne SARs, this Special Issue covers broad topics including but not limited to system design and analysis, imaging algorithms, related applications such as deformation measurements, tomography, moving target detection, etc.

Dr. Xichao Dong
Dr. Yuanhao Li
Prof. Dr. Cheng Hu
Prof. Dr. Andrea Monti Guarnieri
Guest Editors

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Keywords

Synthetic aperture radar (SAR)
Distributed spaceborne SAR
Geosynchronous SAR
Spaceborne–airborne bistatic SAR
Interferometric SAR (InSAR)
SAR tomography
Deformation retrieval
Moving target detection

Published Papers (7 papers)

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Research

22 pages, 16967 KiB

Open AccessArticle

Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target

by Fansen Zhou, Yidi Wang, Wei Zheng, Zhao Li and Xin Wen

Remote Sens. 2022, 14(17), 4239; https://doi.org/10.3390/rs14174239 - 28 Aug 2022

Cited by 2 | Viewed by 1331

Abstract

The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this approach may not work well, due to the unknown maneuvers of the non-cooperative target, and the limited computing power of the satellites. To enhance tracking stability and accuracy, and to lower the computational burden, we have proposed a Fast Distributed Multiple-Model (FDMM) nonlinearity estimation algorithm for satellites, which adopts a novel distributed multiple-model fusion framework. This approach first requires each satellite to perform local filtering based on its own single model, and the corresponding fusion factor derived by the Wasserstein distance is solved for each local estimate; then, after diffusing the local estimates, each satellite performs multiple-model fusion on the received estimates, based on the minimum weighted Kullback–Leibler divergence; finally, each satellite updates its state estimation according to the consensus protocol. Two simulation experiments revealed that the proposed FDMM algorithm outperformed the other four tracking algorithms: the consensus-based distributed multiple-model UKF; the improved consensus-based distributed multiple-model STUKF; the consensus-based strong-tracking adaptive CKF; and the interactive multiple-model adaptive UKF; the FDMM algorithm had high tracking precision and low computational complexity, showing its effectiveness for satellites tracking the near-space target. Full article

(This article belongs to the Special Issue Distributed Spaceborne SAR: Systems, Algorithms, and Applications)

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19 pages, 5284 KiB

Open AccessArticle

Deformation Monitoring of Tailings Reservoir Based on Polarimetric Time Series InSAR: Example of Kafang Tailings Reservoir, China

by Hao Wu, Xiangyuan Zheng, Hongdong Fan and Zeming Tian

Remote Sens. 2022, 14(15), 3655; https://doi.org/10.3390/rs14153655 - 29 Jul 2022

Cited by 1 | Viewed by 1352

Abstract

Safe operation of tailings reservoirs is essential to protect downstream life and property, but current monitoring methods are inadequate in scale and refinement, and most reservoirs are built in low coherence areas far from cities. Use of polarization data to monitor deformation may improve area coherence and thus point selection density. With the example of the Kafang tailings reservoir and dual-polarization Sentinel-1 data from 9 August 2020 to 24 May 2021, homogeneous points of different polarization channels were identified with the hypothesis test of the confidence interval method. Results were fused, and BEST, sub-optimum scattering mechanism (SOM), and equal scattering mechanism (ESM) methods were used to optimize phase quality of persistent scatterer (PS) and distributed scatterer (DS) pixels and obtain more detailed deformation information on the area with time series processing. The fusion of homogeneous point sets obtained from different polarization intensity data increased the number of homogeneous points, which was 3.86% and 8.45% higher than that of VH and VV polarization images, respectively. The three polarization optimization methods improved point selection density. Compared with the VV polarization image, the high coherence point density increased by 1.83 (BEST), 3.66 (SOM), and 5.76 (ESM) times, whereas it increased by 1.17 (BEST), 1.84 (SOM), and 2.04 (ESM) times in the tailings reservoir. The consistency and reliability of different methods were good. By comparing the monitoring results of the three methods using polarization data, the hypothesis test of the confidence interval (HTCI) algorithm, and the polarization optimization method will effectively increase the point selection number of the study area, and the ESM method can show the deformation of tailings area more comprehensively. Monitoring indicated deformation of the tailings reservoir tended to diffuse outward from the area with the largest deformation and was relatively stable. Full article

(This article belongs to the Special Issue Distributed Spaceborne SAR: Systems, Algorithms, and Applications)

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24 pages, 8726 KiB

Open AccessArticle

Dynamic Range Compression Self-Adaption Method for SAR Image Based on Deep Learning

by Hao Shi, Qingqing Sheng, Yupei Wang, Bingying Yue and Liang Chen

Remote Sens. 2022, 14(10), 2338; https://doi.org/10.3390/rs14102338 - 12 May 2022

Cited by 2 | Viewed by 1977

Abstract

The visualization of synthetic aperture radar (SAR) images involves the mapping of high dynamic range (HDR) amplitude values to gray levels for lower dynamic range (LDR) display devices. This dynamic range compression process determines the visibility of details in the displayed result. It therefore plays a critical role in remote sensing applications. There are some problems with existing methods, such as poor adaptability, detail loss, imbalance between contrast improvement and noise suppression. To effectively obtain the images suitable for human observation and subsequent interpretation, we introduce a novel self-adaptive SAR image dynamic range compression method based on deep learning. Its designed objective is to present the maximal amount of information content in the displayed image and eliminate the contradiction between contrast and noise. Considering that, we propose a decomposition-fusion framework. The input SAR image is rescaled to a certain size and then put into a bilateral feature enhancement module to remap high and low frequency features to realize noise suppression and contrast enhancement. Based on the bilateral features, a feature fusion module is employed for feature integration and optimization to achieve a more precise reconstruction result. Visual and quantitative experiments on synthesized and real-world SAR images show that the proposed method notably realizes visualization which exceeds several statistical methods. It has good adaptability and can improve SAR images’ contrast for interpretation. Full article

(This article belongs to the Special Issue Distributed Spaceborne SAR: Systems, Algorithms, and Applications)

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27 pages, 9576 KiB

Open AccessArticle

A Unified Algorithm for the Sliding Spotlight and TOPS Modes Data Processing in Bistatic Configuration of the Geostationary Transmitter with LEO Receivers

by Feng Tian, Zhiyong Suo, Yuekun Wang, Zheng Lu, Zhen Wang and Zhenfang Li

Remote Sens. 2022, 14(9), 2006; https://doi.org/10.3390/rs14092006 - 21 Apr 2022

Cited by 3 | Viewed by 1254

Abstract

This paper deals with the imaging problem for sliding spotlight (SS) and terrain observation by progressive scan (TOPS) modes in bistatic configuration of the geostationary (GEO) transmitter with a low earth orbit satellite (LEO) receiver, named GTLR-BiSAR system. A unified imaging algorithm is proposed to process the GTLR-BiSAR data acquired in SS or TOPS modes. Our main contributions include four aspects. Firstly, the imaging geometry of this novel configuration is described in detail. Furthermore, the GTLR-BiSAR signal expressions were deduced in both time and frequency domains. These signal expressions provide great support for the design of processing the algorithm theoretically. Secondly, we present a unified deramping-based technique according to the special geometry of GTLR-BiSAR to overcome the azimuth spectrum aliasing phenomenon, which typically affects SS and TOPS data. Thirdly, the spatial variance of GTLR-BiSAR data were thoroughly analyzed based on the range-Doppler (RD) geolocation functions. On the basis of a former analysis, we put forward the azimuth variance correction strategy and modified the conventional chirp scaling function to solve the range variance problem. Finally, we completed the derivation of the two-dimensional spectrum after the range chirp scaling. On the basis of spectrum expressions, we compensated for the quadratic and residue phase, and the azimuth compression was completed by SPECAN operation. In addition, we provide a flow diagram to visually exhibit the processing procedures. At the end of this paper, the simulation and real data experiment results are presented to validate the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Distributed Spaceborne SAR: Systems, Algorithms, and Applications)

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21 pages, 29599 KiB

Open AccessArticle

A Multicomponent Temporal Coherence Model for 3-D Phase Unwrapping in Time-Series InSAR of Seasonal Deformation Areas

by Bo Yang, Huaping Xu, Liming Jiang, Ronggang Huang, Zhiwei Zhou, Hansheng Wang and Wei Liu

Remote Sens. 2022, 14(5), 1080; https://doi.org/10.3390/rs14051080 - 22 Feb 2022

Cited by 4 | Viewed by 1971

Abstract

3-D phase unwrapping (PU) methods based on the 2-D linear temporal coherencemodel have been widely used in time-series interferometric synthetic aperture radar (TS-InSAR) for measuring topography and monitoring subtle deformation. However, the linear temporal coherencemodel can not characterize the coherence of highly coherent pixels accurately in seasonal deformation areas, where nonlinear deformation is deterministic and nonnegligible. Especially, for urban areas with groundwater or thermal dilation seasonal changes or permafrost regions, the nonlinear deformation is usually associated with periodic temperature changes. In this work, a general multi-component temporal coherence model, which considers multiple components including the seasonal deformation, is proposed for 3-D PU of seasonal deformation areas. Moreover, the uncertainty evaluation criterion, based on Cramér–Rao bound (CRB), is derived for TS-InSAR. The experimental results, obtained by applying the multi-component temporal coherence model to a data set acquired from January 2012 to February 2016 over the Beijing Capital International Airport area, confirm the effectiveness of the proposed method. High phase consistency, accurate corrected digital elevation model (DEM) and deformation information monitoring with high-density and high-coverage PS pixels are achieved. Under the same iterations and TS-InSAR procedure, the enhanced performance by the proposed model is illustrated by comparing with that of linear model in terms of phase consistency of 3-D phase unwrapping, PSCs selection at each step, and final results evaluation. In summary, the number of phase-consistency edges after 3-D PU is increased by about 15%, the number of final PS pixels selected with the same coherence threshold constraint is increased by about 10%, and more PS pixels provide a low uncertainty in residual topography, mean deformation velocity and seasonal amplitude estimation. Full article

(This article belongs to the Special Issue Distributed Spaceborne SAR: Systems, Algorithms, and Applications)

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24 pages, 10426 KiB

Open AccessArticle

A Scale-Aware Pyramid Network for Multi-Scale Object Detection in SAR Images

by Linbo Tang, Wei Tang, Xin Qu, Yuqi Han, Wenzheng Wang and Baojun Zhao

Remote Sens. 2022, 14(4), 973; https://doi.org/10.3390/rs14040973 - 16 Feb 2022

Cited by 28 | Viewed by 3863

Abstract

Multi-scale object detection within Synthetic Aperture Radar (SAR) images has become a research hotspot in SAR image interpretation. Over the past few years, CNN-based detectors have advanced sharply in SAR object detection. However, the state-of-the-art detection methods are continuously limited in Feature Pyramid Network (FPN) designing and detection anchor setting aspects due to feature misalignment and targets’ appearance variation (i.e., scale change, aspect ratio change). To address the mentioned limitations, a scale-aware feature pyramid network (SARFNet) is proposed in this study, which comprises a scale-adaptive feature extraction module and a learnable anchor assignment strategy. To be specific, an enhanced feature pyramid sub-network is developed by introducing a feature alignment module to estimate the pixel offset and contextually align the high-level features. Moreover, a scale-equalizing pyramid convolution is built through 3-D convolution within the feature pyramid to improve inter-scale correlation at different feature levels. Furthermore, a self-learning anchor assignment is set to update hand-crafted anchor assignments to learnable anchor/feature configuration. By using the dynamic anchors, the detector of this study is capable of flexibly matching the target with different appearance changes. According to extensive experiments on public SAR image data sets (SSDD and HRSID), our algorithm is demonstrated to outperform existing boat detectors. Full article

(This article belongs to the Special Issue Distributed Spaceborne SAR: Systems, Algorithms, and Applications)

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22 pages, 7297 KiB

Open AccessArticle

A Long-Time Coherent Integration STAP for GEO Spaceborne-Airborne Bistatic SAR

by Chang Cui, Xichao Dong, Zhiyang Chen, Cheng Hu and Weiming Tian

Remote Sens. 2022, 14(3), 593; https://doi.org/10.3390/rs14030593 - 26 Jan 2022

Cited by 8 | Viewed by 2091

Abstract

A geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-BSAR) system is an important technique to achieve long-time moving target monitoring over a wide area. However, due to special bistatic configuration of GEO SA-BSAR, two major challenges, i.e., severe range migration and space-variant Doppler parameters for moving targets, hinder the moving target indication (MTI) processing. Traditional SAR MTI methods, which do not take the challenges into consideration, will defocus the moving targets, leading to a loss of the signal-to-noise ratio (SNR). To focus moving targets and estimate motion parameters accurately, long-time coherent integration space-time adaptive processing (LTCI-STAP) is proposed for GEO SA-BSAR MTI in this paper. First, a modified adaptive spatial filtering based on the bistatic signal model is performed to suppress the clutter. Then, an LTCI filter bank is constructed to achieve range migration correction and moving target focusing, which yields the optimal output signal and filtering parameters. Finally, constant false alarm rate (CFAR) detection is carried out to determine the targets, and the space-variant Doppler parameters, solved from the filtering parameters, are used for estimating moving target positions and velocities. Simulations verify the effectiveness of our method. Full article

(This article belongs to the Special Issue Distributed Spaceborne SAR: Systems, Algorithms, and Applications)

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