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18 pages, 9382 KiB  
Article
A Novel In Vitro Primary Human Alveolar Model (AlveolAir™) for H1N1 and SARS-CoV-2 Infection and Antiviral Screening
by Cindia Ferreira Lopes, Emilie Laurent, Mireille Caul-Futy, Julia Dubois, Chloé Mialon, Caroline Chojnacki, Edouard Sage, Bernadett Boda, Song Huang, Manuel Rosa-Calatrava and Samuel Constant
Microorganisms 2025, 13(3), 572; https://doi.org/10.3390/microorganisms13030572 - 3 Mar 2025
Viewed by 314
Abstract
Lower respiratory infections, mostly caused by viral or bacterial pathogens, remain a leading global cause of mortality. The differences between animal models and humans contribute to inefficiencies in drug development, highlighting the need for more relevant and predictive, non-animal models. In this context, [...] Read more.
Lower respiratory infections, mostly caused by viral or bacterial pathogens, remain a leading global cause of mortality. The differences between animal models and humans contribute to inefficiencies in drug development, highlighting the need for more relevant and predictive, non-animal models. In this context, AlveolAir™, a fully primary in vitro 3D human alveolar model, was characterized and demonstrated the sustained presence of alveolar type I (ATI) and type II (ATII) cells. This model exhibited a functional barrier over a 30-day period, evidenced by high transepithelial electrical resistance (TEER). These findings were further validated by tight junctions’ confocal microscopy and low permeability to Lucifer yellow, confirming AlveolAir™ as robust platform for drug transport assays. Additionally, successful infections with H1N1 and SARS-CoV-2 viruses were achieved, and antiviral treatments with Baloxavir and Remdesivir, respectively, effectively reduced viral replication. Interestingly, both viruses infected only the epithelial layer without replicating in endothelial cells. These findings indicate AlveolAir™ as a relevant model for assessing the toxicity and permeability of xenobiotics and evaluating the efficacy of novel antiviral therapies. Full article
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Figure 1
<p>Morphological characterization of AlveolAir™. (<b>A</b>) Representative cross-section of AlveolAir™, on day 11, stained with hematoxylin and eosin. (<b>B</b>,<b>C</b>) Transmission electron microscopy (TEM) images of AlveolAir™ cross-sections on days 20 and 18, respectively. (<b>C</b>) Arrowheads show lamellar bodies. (<b>D</b>,<b>E</b>) Representative confocal immunofluorescence staining on day 18 of pneumocytes type 2 (ATII) protein HTII-280 (red) and pneumocyte type 1 (ATI) protein Podoplanin (PDPN, green) and nuclei (DAPI, blue). Acquisition of representative images was performed with a confocal inverted microscope Zeiss Confocal LSM 800 (Zeiss, Oberkochen, Germany). The image is displayed in the X-Y projection, accompanied by orthogonal views positioned above and to the right of the main image. A red bar is included to indicate the Z position corresponding to the X-Y projection.</p>
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<p>Molecular characterization of AlveolAir™. The relative quantification of gene expression from one sample of human lung parenchyma was correlated to gene expression in AlveolAir™. (<b>A</b>,<b>B</b>) Epithelial tissues were lysed at days 18 and 28, and from the purified RNA, the genes of interest were measured by RT-qPCR (n = 3, mean ± SEM). Relative gene expression was normalized by GAPDH house-keeping gene and presented as mean fold-change compared to human lung parenchyma. (<b>A</b>) ATI markers AQP5, CAV-1, and PDPN gene expression were assessed. (<b>B</b>) ATII markers ABCA3 and HHIP gene expression were evaluated. (<b>C</b>) HTII-280-positive cells were visualized by IF and their surface quantified and expressed as a percentage of apical cell surface (n = 3, mean ± SEM).</p>
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<p>AlveolAir™ barrier functions. (<b>A</b>) Scanning electron microscopy (SEM) images of AlveolAir™ apical surface on day 18, arrowheads indicating tight junctions between cells. (<b>B</b>,<b>C</b>) Acquisition of representative images areas was performed with a confocal inverted microscope Zeiss Confocal LSM 800. (<b>B</b>) Immunofluorescence staining of adherent junctions (E-cadherin, red) and nucleus (DAPI, blue). The image is displayed in the X-Y projection, accompanied by orthogonal views positioned above and to the right of the main image. A white bar is included to indicate the Z position corresponding to the X-Y projection. (<b>C</b>) Immunofluorescence staining of tight junctions (ZO-1, green). (<b>D</b>) Kinetic analysis of transepithelial electrical resistance (TEER) (n = 7, mean ± SEM). (<b>E</b>) Permeability of AlveolAir™ to Lucifer yellow (n = 3, mean ± SEM).</p>
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<p>Characterization of H1N1 infection in AlveolAir™ model. AlveolAir™ epithelium was infected with H1N1 A/Switzerland/3076/2016 strain at a MOI of 1.6 × 10<sup>−4</sup> (n = 6) or mock-inoculated with Opti-MEM<sup>®</sup> medium (non-infected, NI, n = 3). Baloxavir treatment (10 nM) was administered in the basal medium of infected AlveolAir™ at 0 h post-infection (hpi), 24 hpi, 48 hpi, and 72 hpi (n = 3). As control for inflammation, non-infected AlveolAir™ were exposed to a cytomix mixture of LPS, TNF-α, and FCS in basal medium at different time points (n = 3). (<b>A</b>) TEER was measured daily from 0 hpi to 96 hpi and expressed as Ω∙cm<sup>2</sup>. (<b>B</b>) Infectious titers were measured in MDCK cells from apical washes harvested daily by TCID50 assays. (<b>C</b>,<b>D</b>) Virus replication was measured by RT-qPCR quantification of viral M gene from (<b>C</b>) apical wash samples harvested daily after infection or (<b>D</b>) cellular lysates of alveolar cells (Alv.) or endothelial cells (Endo.) harvested at 96 hpi. Absolute M gene quantification was calculated using a standard curve. (<b>E</b>) Host genes (IL-6, IL-8, CXCL10, RANTES, IFNL1, and IFNB1) expression after infection and/or treatment were measured by RT-qPCR from cellular lysates of alveolar cells at 96 hpi. Relative gene expression was normalized on GAPDH expression and represented as mean fold-change compared to NI condition. (<b>F</b>,<b>H</b>) IL-6 (<b>F</b>), IL-8 (<b>G</b>), and RANTES (<b>H</b>) cytokines were quantified by ELISA assays from basal samples. Results are represented as mean ± SEM. Statistical significance was calculated by two-way ANOVA in comparison to H1N1 group (<b>B</b>,<b>C</b>) or NI group (<b>F</b>–<b>H</b>) or by one-way ANOVA in comparison to NI group ((<b>D</b>,<b>E</b>); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Characterization of SARS-CoV-2 infection in AlveolAir<sup>TM</sup> model. AlveolAir™ cultures (n = 3/condition) were infected with SARS-CoV-2 Delta strain (B.1.617.2) at a MOI of 0.1 (n = 5) or mock-inoculated with Opti-MEM<sup>®</sup> medium (non-infected, NI, n = 3). Remdesivir treatment (5 µM) was administered in the basal medium 1 h post-infection (hpi), 24 hpi, 48 hpi, and 72 hpi (n = 3). As control for inflammation, non-infected AlveolAir™ cultures were exposed to a cytomix mixture of LPS, TNF-α, and FCS in basal medium at different time points (n = 3). (<b>A</b>) TEER was measured daily from 0 hpi to 96 hpi and expressed as Ω∙cm<sup>2</sup>. (<b>B</b>) Infectious titers were measured in Vero E6 cells from apical washes harvested daily by TCID<sub>50</sub> assays. (<b>C</b>,<b>D</b>) Virus replication was measured by RT-qPCR quantification of viral nsp14 gene from (<b>C</b>) apical wash samples harvested daily after infection or (<b>D</b>) cellular lysates of alveolar cells (Alv.) or endothelial cells (Endo.) harvested at 96 hpi. Absolute nsp14 gene quantification was calculated using a standard curve. (<b>E</b>) Host genes (IL-6, IL-8, CXCL10, RANTES, IFNL1, and IFNB1) expression after infection and/or treatment were measured by RT-qPCR from cellular lysates of alveolar cells at 96 hpi. Relative gene expression was normalized on GAPDH expression and represented as mean fold-change compared to NI condition. (<b>F</b>–<b>H</b>) IL-6 (<b>F</b>), IL-8 (<b>G</b>), and RANTES (<b>H</b>) cytokines were quantified by ELISA assays from basal samples. Results are represented as mean ± SEM. Statistical significance was calculated by two-way ANOVA in comparison to the H1N1 group (<b>B</b>,<b>C</b>) or NI group (F-H) or by one-way ANOVA in comparison to the NI group ((<b>D</b>,<b>E</b>); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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21 pages, 15639 KiB  
Article
First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
by Bo Pan, Xinzhe Yuan, Tao Li, Tao Lai, Xiaoqing Wang, Chengji Xu and Haifeng Huang
Remote Sens. 2025, 17(1), 131; https://doi.org/10.3390/rs17010131 - 2 Jan 2025
Viewed by 601
Abstract
The inversion of ocean currents is a significant challenge and area of interest in ocean remote sensing. Spaceborne along-track interferometric synthetic aperture radar (ATI-SAR) has several advantages and benefits, including precise observations, extensive swath coverage, and high resolution. However, a limited number of [...] Read more.
The inversion of ocean currents is a significant challenge and area of interest in ocean remote sensing. Spaceborne along-track interferometric synthetic aperture radar (ATI-SAR) has several advantages and benefits, including precise observations, extensive swath coverage, and high resolution. However, a limited number of spaceborne interferometric synthetic aperture radar (InSAR) systems are operating in orbit. Among these, the along-track baseline length is generally suboptimal, resulting in low inversion accuracy and difficulty in achieving operational stability. One of the approaches involves employing lower-frequency bands such as the L band to increase the baseline length to achieve the optimal baseline for a satellite formation. The LuTan-1 mission, the world’s first L-band distributed spaceborne InSAR system, was successfully launched on 27 February 2022. L-band distributed formation operation provides insight into the development of future spaceborne ATI systems with application to new exploration regimes and under optimal baseline conditions. There are two novel aspects of this investigation: (1) We described the ocean current inversion process and results based on LuTan-1 SAR data for the first time. (2) A cross-track baseline component phase removal method based on parameterized modeling was proposed for distributed InSAR systems. Both qualitative and quantitative comparisons validated the effectiveness and accuracy of the inversion results. Full article
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<p>Schematic of along-track interferometry geometry.</p>
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<p>Schematic of across-track interferometry geometry.</p>
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<p>Geographical location of study area.</p>
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<p>(<b>a</b>) Intensity of primary image. (<b>b</b>) Intensity of secondary image.</p>
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<p>Reference data. (<b>a</b>) ECMWF wind field at 10 m height above sea surface. (<b>b</b>) CMEMS sea surface velocity. (<b>c</b>) CMEMS sea surface height.</p>
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<p>Flowchart of surface current extraction processor for distributed SAR interferometry.</p>
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<p>(<b>a</b>) Correlation coefficient between primary and secondary images. (<b>b</b>) Interferometric phase diagram. The red box in the left column indicates the area corresponding to the interference fringes on the right.</p>
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<p>Acquisition geometry of hybrid interferometric SAR.</p>
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<p>(<b>a</b>) The residual fringe after the orbit-based method. (<b>b</b>) The fitted flat Earth phase. (<b>c</b>) The re-flattened interferograms. The color map represents the interferometric phase.</p>
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<p>M4S model ATI-SAR ocean surface retrieval process.</p>
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<p>(<b>a</b>) Variation in RMSE with number of iterations. (<b>b</b>) Percentage of points that meet convergence conditions with increasing number of iterations.</p>
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<p>(<b>a</b>) Sea surface velocity from LuTan-1. (<b>b</b>) Sea surface velocity reference data from CMEMS. Color map represents radial current velocity.</p>
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<p>A scatter diagram of the LuTan-1 ATI currents versus the CMEMS reference data. The color map shows the point density per pixel. The red line represents the reference line where <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math>.</p>
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<p>The sea surface velocity retrieved by ATI. The color map represents the radial current velocity.</p>
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<p>Enlarged views of selected subregions from <a href="#remotesensing-17-00131-f014" class="html-fig">Figure 14</a>: (<b>a</b>) Region 1, (<b>b</b>) Region 2. The color map represents the radial current velocity.</p>
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23 pages, 17457 KiB  
Article
Research on Digital Twin Method for Spaceborne Along-Track Interferometric Synthetic Aperture Radar Velocity Inversion of Ocean Surface Currents
by Zhou Min, He Yan, Xinrui Jiang, Xin Chen, Junyi Zhou and Daiyin Zhu
Remote Sens. 2024, 16(19), 3739; https://doi.org/10.3390/rs16193739 - 8 Oct 2024
Viewed by 1045
Abstract
In this paper, an end-to-end system framework is proposed for the Digital Twin study of spaceborne ATI-SAR ocean current velocity inversion. Within this framework, a fitting inversion approach is proposed to enhance the conventional spaceborne ATI-SAR ocean current velocity inversion algorithm. Consequently, the [...] Read more.
In this paper, an end-to-end system framework is proposed for the Digital Twin study of spaceborne ATI-SAR ocean current velocity inversion. Within this framework, a fitting inversion approach is proposed to enhance the conventional spaceborne ATI-SAR ocean current velocity inversion algorithm. Consequently, the issue of possible local inversion errors stemming from the mismatch between the traditional spaceborne ATI-SAR inversion algorithm and various dual-antenna configurations is resolved to a certain extent. A simulated spaceborne ATI-SAR system, featuring a dual-antenna configuration comprising a baseline direction perpendicular to the track and a squint angle, is presented to validate the efficacy of the Digital Twin methodology. Under the specified simulation parameters, the average inversion error for the final ocean current velocity is recorded at 0.0084 m/s, showcasing a reduction of 0.0401 m/s compared with the average inversion error prior to optimization. Full article
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<p>Spaceborne ATI-SAR ocean surface current inversion Digital Twin system framework.</p>
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<p>Observation geometry between spaceborne ATI-SAR platform and ocean surface triangular scattering unit.</p>
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<p>Time−varying ocean surface echo simulation system.</p>
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<p>Any dual-antenna configuration.</p>
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<p>Observation geometry of RTPC algorithm.</p>
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<p>Framework of ocean surface current fitting inversion system based on Digital Twin.</p>
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<p>Antenna configuration and operational mode: (<b>a</b>) antenna configuration; (<b>b</b>) operational mode.</p>
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<p>3D modeling outcomes of the ocean surface: (<b>a</b>) Group 1 3D ocean surface. (<b>b</b>) Group 1 2D ocean surface top view heat map. (<b>c</b>) Group 1 backscattering coefficient. (<b>d</b>) Group 2 3D ocean surface. (<b>e</b>) 2D ocean surface top view heat map. (<b>f</b>) Group 2 backscattering coefficient. (<b>g</b>) Group 3 3D ocean surface. (<b>h</b>) Group 3 2D ocean surface top view heat map. (<b>i</b>) Group 3 backscattering coefficient.</p>
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<p>BP images of ocean surface: (<b>a</b>) Group 1. (<b>b</b>) Group 2. (<b>c</b>) Group 3. (<b>d</b>) Group 4. (<b>e</b>) Group 5. (<b>f</b>) Group 6. (<b>g</b>) Group 7. (<b>h</b>) Group 8. (<b>i</b>) Group 9.</p>
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<p>Interference phase: (<b>a</b>) Theoretical interference phase. (<b>b</b>) Actually obtained interference phase in subgroup 1. (<b>c</b>) Actually obtained interference phase in subgroup 2. (<b>d</b>) Actually obtained interference phase in subgroup 3.</p>
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<p>Radial velocity−interference phase fitting curve based on Digital Twin.</p>
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<p>Theoretical ocean surface current field: (<b>a</b>) Theoretical current field of three kinds of radial velocity. (<b>b</b>) Mean profile in range direction of the theoretical current field.</p>
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<p>Comparison of the initially guessed ocean current field before and after fitting curve optimization in subgroup 1: (<b>a</b>) Before fitting curve optimization. (<b>b</b>) Mean profile in range direction before fitting curve optimization. (<b>c</b>) After fitting curve optimization. (<b>d</b>) Mean profile in range direction after fitting curve optimization.</p>
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<p>Comparison of the initially guessed ocean current field before and after fitting curve optimization in subgroup 2: (<b>a</b>) Before fitting curve optimization. (<b>b</b>) Mean profile in range direction before fitting curve optimization. (<b>c</b>) After fitting curve optimization. (<b>d</b>) Mean profile in range direction after fitting curve optimization.</p>
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<p>Comparison of the initially guessed ocean current field before and after fitting curve optimization in subgroup 3: (<b>a</b>) Before fitting curve optimization. (<b>b</b>) Mean profile in range direction before fitting curve optimization. (<b>c</b>) After fitting curve optimization. (<b>d</b>) Mean profile in range direction after fitting curve optimization.</p>
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<p>Comparison of the best iterative ocean current field before and after fitting curve optimization in subgroup 1.</p>
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<p>Comparison of the best iterative ocean current field before and after fitting curve optimization in subgroup 2.</p>
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<p>Comparison of the best iterative ocean current field before and after fitting curve optimization in subgroup 3.</p>
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21 pages, 13643 KiB  
Article
Strong Clutter Suppression Using Spatial and Signal Similarity for Multi-Channel SAR Ground-Moving-Target Indication
by Qinghai Dong, Wei Li, Ruihua Shi, Ke Wang, Bingnan Wang, Chen Song, Chong Song and Maosheng Xiang
Remote Sens. 2023, 15(20), 4913; https://doi.org/10.3390/rs15204913 - 11 Oct 2023
Viewed by 1332
Abstract
This paper presents a new two-stage approach for suppressing strong clutter and detecting moving targets using scatterers’ spatial structure and signal similarity. Compared with the traditional strong clutter suppression methods, the proposed method considers both the spatial similarity and the channel correlation of [...] Read more.
This paper presents a new two-stage approach for suppressing strong clutter and detecting moving targets using scatterers’ spatial structure and signal similarity. Compared with the traditional strong clutter suppression methods, the proposed method considers both the spatial similarity and the channel correlation of the scatterers, effectively alleviating the false alarm probability and avoiding the missed detection problem caused via identifying strong moving targets as strong stationary clutter. Additionally, a detector is presented based on the linear degree of the radial velocity interferometric phase (LDRVP) to eliminate false alarms from isolated strong scatter points and the edges of strong scatterers. The experimental results of the X-band radar indicate the presented approach’s lower false alarm probability and superior robustness. Full article
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Graphical abstract

Graphical abstract
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<p>The multichannel SAR-GMTI’s observation geometry.</p>
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<p>Spatial similarity estimation vector.</p>
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<p>The efficiency analysis under hypothesis <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mn>1</mn> </msub> </mrow> </semantics></math>. (<b>a</b>) ROC of five identification approaches attained by Monte Carlo simulations; (<b>b</b>) ROC of two identification approaches with various values of <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mrow> <mi>v</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>The flowchart of the presented approach. The red square denotes the pixels of the moving targets detected, and the green square denotes the pixels of false alarms.</p>
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<p>Comparison of six algorithms’ computational complexity. (<b>a</b>) The FLOP curves of six methods; (<b>b</b>) local magnification of the blue box area.</p>
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<p>Platform and cooperative targets. (<b>a</b>) The airplane employed in the test; (<b>b</b>) the cooperative targets.</p>
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<p>The SAR image of the observation scene.</p>
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<p>Characteristic analysis of strong scatterers. (<b>a</b>) Strong scatterer; (<b>b</b>) correlation coefficient; (<b>c</b>) the correlation coefficient’s statistical histograms; (<b>d</b>) interferometric phase; (<b>e</b>) statistical histograms of the interferometric phase; (<b>f</b>) the DPCA result.</p>
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<p>Characteristic analysis of strong scatterers. (<b>a</b>) Strong scatterer; (<b>b</b>) correlation coefficient; (<b>c</b>) the correlation coefficient’s statistical histograms; (<b>d</b>) interferometric phase; (<b>e</b>) statistical histograms of the interferometric phase; (<b>f</b>) the DPCA result.</p>
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<p>Strong scatterer suppression out moving target region. (<b>a</b>) DPCA test results; (<b>b</b>) strong scatter location extraction results; (<b>c</b>) strong scatterer suppression results of the presented approach; (<b>d</b>) detection results of Stage I.</p>
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<p>Strong scatterer suppression in the target region. (<b>a</b>) SAR image; (<b>b</b>) DPCA results.</p>
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<p>Strong scatterer suppression in the target region. (<b>a</b>) The clutter extracted by the method in [<a href="#B31-remotesensing-15-04913" class="html-bibr">31</a>]; (<b>b</b>) the strong scatterer clutter extracted by the presented approach; (<b>c</b>) the clutter alleviation results of the approach in [<a href="#B31-remotesensing-15-04913" class="html-bibr">31</a>]; (<b>d</b>) the clutter suppression results of the presented approach.</p>
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<p>Strong scatterer suppression in the target region. (<b>a</b>) The clutter extracted by the method in [<a href="#B31-remotesensing-15-04913" class="html-bibr">31</a>]; (<b>b</b>) the strong scatterer clutter extracted by the presented approach; (<b>c</b>) the clutter alleviation results of the approach in [<a href="#B31-remotesensing-15-04913" class="html-bibr">31</a>]; (<b>d</b>) the clutter suppression results of the presented approach.</p>
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<p>The results of the two methods. (<b>a</b>) The approach in [<a href="#B31-remotesensing-15-04913" class="html-bibr">31</a>]; (<b>b</b>) the presented approach.</p>
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<p>Clutter suppression and moving target detection results for <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>45</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>fa</mi> </mrow> </msub> <mo>=</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>7</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>The number of detected targets in the X-band measured data. (<b>a</b>) DRVC and presented approach; (<b>b</b>) the curves of <math display="inline"><semantics> <mi>K</mi> </semantics></math> and detected target under different values of Pa.</p>
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18 pages, 10852 KiB  
Article
Study on the Elimination Method of Wind Field Influence in Retrieving a Sea Surface Current Field
by Xinzhe Yuan, Jian Wang, Bing Han and Xiaoqing Wang
Sensors 2022, 22(22), 8781; https://doi.org/10.3390/s22228781 - 14 Nov 2022
Viewed by 1692
Abstract
An along-the-track interferometric synthetic aperture radar (ATI-SAR) system can estimate the radial velocity of a moving target on the ground and on a sea surface current. This acquires the interference phase by combining two composite SAR images obtained by two antennas spatially separated [...] Read more.
An along-the-track interferometric synthetic aperture radar (ATI-SAR) system can estimate the radial velocity of a moving target on the ground and on a sea surface current. This acquires the interference phase by combining two composite SAR images obtained by two antennas spatially separated along the direction of movement of the platform. The key to retrieving the sea surface current is to remove the interference of sea surface waves, wind-generated current, and Bragg phase velocity in the interference Doppler velocity. Previous methods removed the surface waves, Bragg phase velocity, and other interferences based on externally-assisted wind fields (e.g., ECMWF), using the M4S or other models. However, the wind fields obtained from ECMWF and other external information are often average results of a large temporal and spatial scale, while the images obtained from SAR are high-resolution images of sea surface transients, which are quite different in time and space. This paper takes the SAR image data of the Gaofen-3 satellite as the research object and employs an SAR-based wind field retrieval method to obtain an SAR-observed transient wind field. Combined with the CDOP model, the interference of Doppler velocities, such as the sea surface wave, wind-generated current, and Bragg wave phase velocity, was calculated and subtracted from the Doppler velocity, to obtain the sea surface velocity result. Then, the current field measured by the shore-based HF radar was compared with that obtained by correcting the ATI Doppler velocity based on the SAR retrieved wind field and the ECMWF wind field. The comparison of results indicated that the wind field correction result based on the SAR retrieved wind field was closer to the current field measured by the shore-based HF radar than the wind field correction result based on the ECMWF wind field. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Microwave Sea Remote Sensing)
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Figure 1
<p>GF-3 satellite observation area (red) and HFSWR measurement area (blue) in the experiment conducted in Qing Dao in 2022.</p>
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<p>The schematic diagram of the GF−3 satellite SAR-ATI mode.</p>
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<p>The input wind field of the five scenes. (<b>a</b>–<b>e</b>) are the wind fields in red square area of <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>.</p>
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<p>The input wind field of the five scenes. (<b>a</b>–<b>e</b>) are the wind fields in red square area of <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>.</p>
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<p>(<b>a</b>) The ECMWF reanalysis wind field of region (<b>b</b>) in the <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>. The ECMWF reanalysis wind field of region c in the <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>.</p>
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<p>Data processing flow.</p>
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<p>(<b>a</b>) The result of HFSWR data processing of scene b in <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>, (<b>b</b>) the result of HFSWR data processing of scene c in <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>.</p>
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<p>NRCS images of the five GF−3 ATI images acquired in Qing Dao. (<b>a</b>–<b>e</b>) are the NRCS of region a to region b in <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>.</p>
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<p>(<b>a</b>–<b>e</b>) are the coherence coefficients of the SLC image pairs of the five scenes after registration in the <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>.</p>
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<p>Retrieved radial velocity of SAR images. (<b>a</b>–<b>e</b>) are the Retrieved radial velocity from the five scenes in the <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>.</p>
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<p>Retrieved radial velocity of SAR images. (<b>a</b>–<b>e</b>) are the Retrieved radial velocity from the five scenes in the <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>.</p>
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<p>Results of retrieving the Doppler anomaly <math display="inline"><semantics> <mrow> <msup> <mi>f</mi> <mrow> <mi>D</mi> <mi>A</mi> </mrow> </msup> </mrow> </semantics></math> using the CDOP model. (<b>a</b>–<b>e</b>) are retrieving the Doppler anomaly results by using the CDOP model from the five scenes in the <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>.</p>
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<p>Results of retrieving the ocean surface currents. (<b>a</b>–<b>e</b>) are retrieving the ocean surface currents from the five scenes in the <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>.</p>
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<p>Scatter diagrams of the GF−3 ATI currents processed with the retrieved wind field versus the HFSWR data for the two scenes: (<b>a</b>) represents scene b; (<b>b</b>) represents scene c.</p>
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<p>Scatter diagrams of GF−3 ATI currents processed with the ECWMF wind field versus the HFSWR data for the two scenes: (<b>a</b>) represents scene b; (<b>b</b>) represents scenes c in the <a href="#sensors-22-08781-f001" class="html-fig">Figure 1</a>.</p>
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21 pages, 6711 KiB  
Article
Influence of Radar Parameters and Sea State on Wind Wave-Induced Velocity in C-Band ATI SAR Ocean Surface Currents
by Rui Zhang, Jie Zhang, Xi Zhang, Chenghui Cao, Xiaochen Wang, Gui Gao, Genwang Liu and Meng Bao
Remote Sens. 2022, 14(17), 4135; https://doi.org/10.3390/rs14174135 - 23 Aug 2022
Cited by 3 | Viewed by 2154
Abstract
Wind wave-induced artifact surface velocity (WASV) is an important component of the sea surface motions detected by synthetic aperture radar (SAR) systems. Understanding the characteristics of the interference of WASV on SAR current velocity estimates is necessary to improve the accuracy of retrievals. [...] Read more.
Wind wave-induced artifact surface velocity (WASV) is an important component of the sea surface motions detected by synthetic aperture radar (SAR) systems. Understanding the characteristics of the interference of WASV on SAR current velocity estimates is necessary to improve the accuracy of retrievals. In this study, we assessed and analyzed the sensitivity of WASV in C-band along-track interferometric (ATI) SAR to radar configuration, wind field, swell field, and a wave spectrum model. Results showed that the influence of wind speed on WASV increased with the current velocity. The swell also affected WASV, especially at higher wind speeds; WASV was more strongly influenced by swell amplitude than by swell wavelength. In terms of radar configurations, results showed that VV polarization was more suitable than HH polarization in the estimation of WASV. The interference of WASV was minimal at moderate incidence angles (around 40°), and an appropriate ATI baseline selection was also given. The WASV was more strongly influenced by sea states than by the wave spectrum model or by a spreading function. The findings of this study improve our understanding of WASV and provide a reference for the design of future ATI SAR current measurement instruments and projects. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)
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<p>The different phases of the study.</p>
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<p>An example of the results of the ocean surface scattering simulations. It includes surface height, true surface radial velocity, the ATI phase for VV polarization, and the estimated radial velocity for VV polarization. In this case, current speed was set to 0.5 m/s, wind speed was 10 m/s, wind direction was 0°, and swell amplitude was 0.5 m.</p>
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<p>The ATI SAR system. Point O is the sub-satellite point and is taken as the origin of the system. The radar flight direction is taken as the positive x axis (range direction). The y axis is perpendicular to the radar flight direction on the horizontal plane (azimuth direction), and the trajectory from O to the measured point indicates the direction of the positive y axis. The z axis is perpendicular to sea level, and the trajectory from O to the satellite indicates the direction of the positive z axis. The platform height is <span class="html-italic">H</span>. The angle of incidence is <span class="html-italic">θ</span>.</p>
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<p>Flow chart of numerical simulation of WASV.</p>
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<p>The relationship between WASV and incidence angle at a wind speed of 10 m/s for HH and VV polarizations. The swell direction was 0°, amplitude was 0.5 m, and wavelength was 200 m. Other input parameters were set to the values indicated in <a href="#remotesensing-14-04135-t001" class="html-table">Table 1</a>. The magnitude of the current velocity is indicated by color: blue indicates the absence of current (0 m/s), yellow indicates moderate current velocity (0.5 m/s), and green indicates high velocity (1.0 m/s). The wind direction is indicated by line style: a solid line indicates downwind direction, a dashed line indicates crosswind direction, and a dotted line indicates upwind direction.</p>
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<p>The relationship between the accuracy of the retrieved velocity as indicated by the ratio between velocity difference and phase difference, Δ<span class="html-italic">v<sub>ATI</sub></span>/Δ<span class="html-italic">ϕ</span>, and radar baseline.</p>
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<p>The relationship between WASV magnitude and baseline at a wind speed of 10 m/s for HH and VV polarizations. The swell direction, amplitude, and wavelength were 0°, 0.5 m, and 200 m, respectively. Other input parameters were set to the values indicated in <a href="#remotesensing-14-04135-t001" class="html-table">Table 1</a>.</p>
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<p>The relationship between WASV and wind direction at an incidence angle of 40° for HH and VV polarizations. The swell direction, amplitude, and wavelength were 0°, 0.5 m, and 200 m, respectively. Other input parameters were set to the values indicated in <a href="#remotesensing-14-04135-t001" class="html-table">Table 1</a>.</p>
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<p>The relationship between WASV magnitude and wind speed for HH and VV polarizations.</p>
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<p>The relationship between WASV and wind speed for HH and VV polarizations. Other input parameters were set to the values indicated in <a href="#remotesensing-14-04135-t001" class="html-table">Table 1</a>.</p>
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<p>The relationship between dimensionless WASV and wind speed for HH and VV polarizations. The swell direction, amplitude, and wavelength were 0°, 0.5 m, and 200 m, respectively. Other input parameters were set to the values indicated in <a href="#remotesensing-14-04135-t001" class="html-table">Table 1</a>. The presence and absence of swell are indicated by line style: solid line indicates no swell, dashed line indicates the presence of swell. The magnitude of the current velocity is indicated by color.</p>
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<p>The relationship between WASV and swell direction for HH and VV polarizations and for different current velocities and swell amplitudes. Wind direction and speed were 0° and 10 m/s, respectively. Swell wavelength was 200 m. Other input parameters were set to the values indicated in <a href="#remotesensing-14-04135-t001" class="html-table">Table 1</a>. The swell amplitude is indicated by color: blue indicates amplitude of 0.5 m, yellow indicates amplitude of 1.0 m, and green indicates amplitude of 1.5 m. Solid line indicates mean values. Shaded areas indicate ±1 standard deviation of the median.</p>
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<p>The relationship between WASV and swell direction for HH and VV polarizations and for different current velocities and swell wavelengths. Wind direction and speed were 0° and 10 m/s, respectively. Swell wavelength was 0.5 m. Other input parameters were set to the values indicated in <a href="#remotesensing-14-04135-t001" class="html-table">Table 1</a>. The swell wavelength is indicated by color: blue indicates wavelength of 150 m, yellow indicates wavelength of 200 m, and green indicates wavelength of 250 m. Solid line indicates mean values. Shaded areas indicate ±1 standard deviation of the median.</p>
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<p>The relationship between WASV and wind direction for different wave spectrum models and current velocities. Wind speed and direction were 10 m/s and 0°, respectively. Swell amplitude, direction, and wavelength were 0.5 m, 0°, and 200 m, respectively. The model is indicated by line style: solid line indicates Elfouhaily, dashed line indicates Jonswap, and dotted line indicates Romeiser97. The current velocity is indicated by color.</p>
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<p>The relationship between WASV and wind direction for different spreading functions and current velocities. Wind speed and direction were 10 m/s and 0°, respectively. Swell amplitude, direction, and wavelength were 0.5 m, 0°, and 200 m, respectively. The spreading function is indicated by color: blue indicates the E spreading function, yellow indicates the H spreading function, and green indicates the R spreading function. The omnidirectional wave spectrum is the typical directional function of the E spreading function.</p>
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23 pages, 2396 KiB  
Article
Soil Moisture Retrieval Using Multistatic L-Band SAR and Effective Roughness Modeling
by Emma Tronquo, Hans Lievens, Jean Bouchat, Pierre Defourny, Nicolas Baghdadi and Niko E. C. Verhoest
Remote Sens. 2022, 14(7), 1650; https://doi.org/10.3390/rs14071650 - 30 Mar 2022
Cited by 9 | Viewed by 3034
Abstract
The interest in bistatic SAR systems for soil moisture monitoring has grown over recent years, since theoretical studies suggest that the impact of surface roughness on the retrieval of soil moisture decreases when multistatic, i.e., simultaneous mono- and bistatic, radar measurements are [...] Read more.
The interest in bistatic SAR systems for soil moisture monitoring has grown over recent years, since theoretical studies suggest that the impact of surface roughness on the retrieval of soil moisture decreases when multistatic, i.e., simultaneous mono- and bistatic, radar measurements are used. This paper presents a semi-empirical method to retrieve soil moisture over bare agricultural fields, based on effective roughness modeling, and applies it to a series of L-band fully-polarized SAR backscatter and bistatic scattering observations. The main advantage of using effective roughness parameters is that surface roughness no longer needs to be measured in the field, what is known to be the main source of error in soil moisture retrieval applications. By means of cross-validation, it is shown that the proposed method results in accurate soil moisture retrieval with an RMSE well below 0.05 m3/m3, with the best performance observed for the cross-polarized backscatter signal. In addition, different experimental SAR monostatic and bistatic configurations are evaluated in this study using the proposed retrieval technique. Results illustrate that the soil moisture retrieval performance increases by using backscatter data in multiple polarizations simultaneously, compared to the case where backscatter observations in only one polarization mode are used. Furthermore, the retrieval performance of a multistatic system has been evaluated and compared to that of a traditional monostatic system. The recent BELSAR campaign (in 2018) provides time-series of experimental airborne SAR measurements in two bistatic geometries, i.e., the across-track (XTI) and along-track (ATI) flight configuration. For both configurations, bistatic observations are available in the backward region. The results show that the simultaneous use of backscatter and bistatic scattering data does not result in a profound increase in retrieval performance for the bistatic configuration flown during BELSAR 2018. As theoretical studies demonstrate a strong improvement in retrieval performance when using backscatter and bistatic scattering coefficients in the forward region simultaneously, the introduction of additional bistatic airborne campaigns with more promising multistatic SAR configurations is highly recommended. Full article
(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)
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<p>Geometry of the bistatic active-passive SAR system.</p>
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<p>The BELAIR Hesbania test site, with the winter wheat and maize fields in green and red, respectively, and all the other agricultural fields inventoried on the anonymous cadastral map for agricultural land provided in Wallonia’s land-parcel identification system (Système Intégré de Gestion et Contôle (SIGeC)) in grey. The four parallel flight tracks are represented by colored arrows.</p>
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<p>Graphical overview of the effective roughness modeling algorithm.</p>
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<p>Simulated versus observed field average bare soil SAR backscatter for HH (<b>left</b>), VV (<b>middle</b>) and HV (<b>right</b>) polarization. Backscatter simulations are performed with the semi-empirical Oh model and in situ measured root-mean-square heights.</p>
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<p>Retrieved field-average soil moisture values using in situ measured root-mean-square heights in the retrieval process with the Oh model against observed field-average soil moisture values, using single-polarized backscatter data.</p>
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<p>Kling–Gupta Efficiency (KGE) values for a range of linear regression models (slope <span class="html-italic">a</span> ranging from 0.001 to 0.20 and intercept <span class="html-italic">b</span> ranging from 0 to 8), obtained by inverting the Oh model for HH (<b>left</b>) VV (<b>middle</b>) and HV (<b>right</b>) normalized bias-corrected backscatter coefficients.</p>
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<p>Simulated versus observed field average bare soil SAR backscatter for HH (<b>left</b>), VV (<b>middle</b>) and HV (<b>right</b>) polarization. Backscatter simulations are performed with the physically-based AIEM and in situ measured roughness parameters.</p>
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<p>Simulated versus observed field average bare soil SAR bistatic scattering in XTI flight configuration for HH (<b>left</b>) and VV (<b>right</b>) polarization. Bistatic scattering simulations are performed with the physically-based AIEM and in situ measured roughness parameters.</p>
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<p>Simulated versus observed field average bare soil SAR bistatic scattering in ATI flight configuration for HH (<b>left</b>) and VV (<b>right</b>) polarization. Bistatic scattering simulations are performed with the physically-based AIEM and in situ measured roughness parameters.</p>
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<p>Simulated field-average soil moisture values based on effective roughness modeling for the Oh model and AIEM against observed field-average soil moisture values for HH (<b>left</b>), VV (<b>middle</b>) and HV (<b>right</b>) polarization. The top line represents the first validation technique performed with the Oh model. The middle line represents the second validation technique, i.e., <span class="html-italic">leave-one-out</span> cross-validation, performed with the Oh model. The bottom line represents the first validation technique performed with the AIEM.</p>
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<p>Simulated field-average soil moisture values based on effective roughness modeling for the Oh model (<b>left</b>) and AIEM (<b>right</b>) against observed field-average soil moisture values, using multi-polarized backscatter data.</p>
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<p>Simulated field-average soil moisture values based on effective roughness modeling for the AIEM against observed field-average soil moisture values. <b>Left</b>: using HH- and VV-polarized backscatter data of the BELSAR campaign. <b>Middle</b>: using HH- and VV-polarized backscatter and XTI bistatic scattering data of the BELSAR campaign. <b>Right</b>: using HH- and VV-polarized backscatter and ATI bistatic scattering data of the BELSAR campaign.</p>
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28 pages, 53000 KiB  
Article
A Novel Detection Scheme in Image Domain for Multichannel Circular SAR Ground-Moving-Target Indication
by Qinghai Dong, Bingnan Wang, Maosheng Xiang, Zhongbin Wang, Yachao Wang and Chong Song
Sensors 2022, 22(7), 2596; https://doi.org/10.3390/s22072596 - 28 Mar 2022
Cited by 3 | Viewed by 2145
Abstract
Circular synthetic aperture radar (CSAR), which can observe the region of interest for a long time and from multiple angles, offers the opportunity for moving-target detection (MTD). However, traditional MTD methods cannot effectively solve the problem of high probability of false alarm (PFA) [...] Read more.
Circular synthetic aperture radar (CSAR), which can observe the region of interest for a long time and from multiple angles, offers the opportunity for moving-target detection (MTD). However, traditional MTD methods cannot effectively solve the problem of high probability of false alarm (PFA) caused by strong clutter. To mitigate this, a novel, three-step scheme combining clutter background extraction, multichannel clutter suppression, and the degree of linear consistency of radial velocity interferometric phase (DLRVP) test is proposed. In the first step, the spatial similarity of the scatterers and the correlation between sub-aperture images are fused to extract the strong clutter mask prior to clutter suppression. In the second step, using the data remaining after elimination of the background clutter in Step 1, an amplitude-based detector with higher processing gain is utilized to detect potential moving targets. In the third step, a novel test model based on DLRVP is proposed to further reduce the PFA caused by isolated strong scatterers. After the above processing, almost all false alarms are excluded. Measured data verified that the PFA of the proposed method is only 20% that of the comparison method, with improved detection of slow and weakly moving targets and with better robustness. Full article
(This article belongs to the Section Remote Sensors)
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Figure 1
<p>The observation geometry of multichannel CSAR-GMTI. The red dashed line represents the path of a moving target; coordinates <math display="inline"><semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>P</mi> <mn>2</mn> </msub> </semantics></math> represent the locations of the same moving target in different sub-aperture images: (<b>a</b>) lateral view; (<b>b</b>) vertical view.</p>
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<p>The geometry of ATI for CSAR (the red dot represents the center of the observation scene).</p>
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<p>The flowchart of the proposed detection scheme. Each square contains multiple pixels: the ‘1’ square denotes strong clutter, the red square denotes the position of the moving targets detected, and the green square denotes the position of false alarms.</p>
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<p>The principle of Step II and gain curves. <math display="inline"><semantics> <mo>Θ</mo> </semantics></math> corresponds to SP algorithm; <math display="inline"><semantics> <mo>Δ</mo> </semantics></math> corresponds to ID algorithm). (<b>a</b>) The flowchart of Step II; (<b>b</b>) The processing gain curves of moving targets by three methods.</p>
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<p>Clutter suppression and the PDF fitted by step II. (<b>a</b>) X-band 0.3 m resolution SAR image; (<b>b</b>) clutter suppression result; (<b>c</b>) PDF fitting result; (<b>d</b>) amplitude before and after clutter suppression: red and green ellipses indicate the amplitude of strong clutter before and after clutter suppression, respectively.</p>
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<p>Detection results of the two methods: red pixels denote the position of false alarm. (<b>a</b>) Extracted strong clutter background; (<b>b</b>) enlarged view of red rectangular area in (<b>a</b>); (<b>c</b>) detection results of <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>Γ</mo> <mi>D</mi> </mrow> </semantics></math> only by Step II; (<b>d</b>) detection results of <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>Γ</mo> <mi>D</mi> </mrow> </semantics></math> Step I and Step II combined; (<b>e</b>) detection results of Weibull distribution only by Step II; (<b>f</b>) detection results of Weibull Step I and Step II combined.</p>
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<p>The theoretical PDF and histogram of interference phase: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mn>1</mn> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mn>2</mn> </msub> </semantics></math>.</p>
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<p>Simulation analysis of peak position of <math display="inline"><semantics> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>: <math display="inline"><semantics> <msub> <mi>v</mi> <mrow> <mi>t</mi> <mi>r</mi> </mrow> </msub> </semantics></math> = 3 m/s, CNR = 13 dB. (<b>a</b>) The CRB of <math display="inline"><semantics> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> </semantics></math> with different input SCNR; (<b>b</b>) The histogram of <math display="inline"><semantics> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> </semantics></math> under hypothesis <math display="inline"><semantics> <msub> <mi>H</mi> <mn>0</mn> </msub> </semantics></math>; (<b>c</b>) The histogram of <math display="inline"><semantics> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> </semantics></math> under hypothesis <math display="inline"><semantics> <msub> <mi>H</mi> <mn>1</mn> </msub> </semantics></math>; (<b>d</b>) The histogram of <math display="inline"><semantics> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> </semantics></math> with different K.</p>
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<p>The performance analysis under hypothesis <math display="inline"><semantics> <msub> <mi>H</mi> <mn>1</mn> </msub> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> </semantics></math> with different inputs for SCNR, <math display="inline"><semantics> <msub> <mi>v</mi> <mrow> <mi>t</mi> <mi>r</mi> </mrow> </msub> </semantics></math> = 4 m/s, CNR = 13 dB; (<b>b</b>) <math display="inline"><semantics> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> </semantics></math> with different values for radial velocity of moving target, SCR = 10 dB; (<b>c</b>) <math display="inline"><semantics> <mover accent="true"> <mi>β</mi> <mo stretchy="false">^</mo> </mover> </semantics></math> with different input for CNR, SCR = 5 dB; (<b>d</b>) ROC of five detection methods obtained by Monte Carlo simulations, SCR = 0 dB, <math display="inline"><semantics> <msub> <mi>v</mi> <mrow> <mi>t</mi> <mi>r</mi> </mrow> </msub> </semantics></math> = 4 m/s; (<b>e</b>) ROC of two detection methods with different <math display="inline"><semantics> <msub> <mi>σ</mi> <mrow> <mi>v</mi> <mi>r</mi> </mrow> </msub> </semantics></math>.</p>
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<p>Flight platform and cooperative targets: (<b>a</b>) airborne SAR flight platform; (<b>b</b>) cooperative targets.</p>
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<p>An RGB image synthesized from three sub-aperture SAR images with different angles; enlarged views of the moving-target area. The sub-aperture angles corresponding to R, G, and B are 0<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>, 18<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>, and 36<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>, respectively.</p>
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<p>Correlation coefficient and its standard deviation. (<b>a</b>) Mean value of correlation coefficient; (<b>b</b>) standard deviation of correlation coefficient; (<b>c</b>) statistical histograms of correlation coefficient and its standard deviation; (<b>d</b>) correlation coefficients of the stationary target and the moving target vary with the aperture angle.</p>
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<p>Strong clutter (<b>first row</b>) and extracted background (<b>second row</b>); from left to right: villages, houses, vehicles parked at intersections, and metal sprinklers in pools.</p>
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<p>Interferometric phase and clutter suppression results. (<b>a</b>) Interferometric phase of channel 1 and channel 4 (before correction); (<b>b</b>) interferometric phase of channel 1 and channel 4 (after correction); (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>DPCA</mi> <mi>max</mi> </msub> </mrow> </semantics></math>; (<b>d</b>) suppression results in strong clutter areas.</p>
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<p>The clutter suppression results (<b>first row</b>) and interferometric phase (<b>second row</b>) of the target areas.</p>
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<p>The detection results of Experiment B; green dots and green circles represent detected targets. (<b>a</b>) Only Step II; (<b>b</b>) Step I and Step II combined.</p>
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<p>The number of detected targets for various values of K: (<b>a</b>) DRVC and proposed method; (<b>b</b>) under different <math display="inline"><semantics> <msub> <mi>P</mi> <mi>fa</mi> </msub> </semantics></math>.</p>
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<p>The amplitude and phase diagrams of the moving targets (T1∼T6).</p>
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22 pages, 41201 KiB  
Article
A Moving Target Velocity Estimation Method Based on the MC-MASA SAR Mode
by Yamin Wang, Jie Chen, Wei Liu, Chunsheng Li and Wei Yang
Remote Sens. 2021, 13(9), 1632; https://doi.org/10.3390/rs13091632 - 21 Apr 2021
Cited by 5 | Viewed by 2549
Abstract
Imaging position shift based on the multiple azimuth squint angles (MASA) mode is effective for target azimuth velocity estimation, whereas accuracy is low when target range velocity is high. In this paper, the estimation problem for both target azimuth and range velocities is [...] Read more.
Imaging position shift based on the multiple azimuth squint angles (MASA) mode is effective for target azimuth velocity estimation, whereas accuracy is low when target range velocity is high. In this paper, the estimation problem for both target azimuth and range velocities is considered based on the multi-channels MASA (MC-MASA) mode. Firstly, the acquisition geometry of MC-MASA mode and Doppler characteristics of a moving target are analyzed in detail, especially in squint mode. Then, for better moving target estimation, the stationary background clutter is removed using the displacement phase center antenna (DPCA) technique, and the failure in range velocity estimation with sequential SAR images is also discussed. Furthermore, a modified along-track interferometry (ATI) is proposed to preliminarily reconstruct the azimuth-and-range velocity map based on the MC-MASA mode. Since the velocity estimation accuracy is dependent on squint angle and signal-to-clutter ratio (SCR), the circumstances are divided into three cases with different iteration estimation strategies, which could expand the scene application scope of velocity estimation and achieve a high estimation accuracy along both azimuth and range directions. Finally, the performance of the proposed method is demonstrated by experimental results. Full article
(This article belongs to the Section Engineering Remote Sensing)
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Graphical abstract

Graphical abstract
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<p>MC-MASA imaging mode geometry with three channels and two observations.</p>
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<p>The geometry of moving target in the azimuth multi-receiving channel mode.</p>
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<p>Doppler spectrum shift with <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>40</mn> <mo> </mo> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>40</mn> <mo> </mo> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>: (<b>a</b>) Range velocity leads to a basically symmetric <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>d</mi> </msub> </mrow> </semantics></math> variation and azimuth velocity leads to a basically linear variation; (<b>b</b>) Range velocity leads to a basically linear <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>r</mi> </msub> </mrow> </semantics></math> variation and azimuth velocity leads to a basically symmetric variation.</p>
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<p>The effect of range velocity on azimuth velocity estimation with <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>20</mn> <mo> </mo> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>40</mn> <mo> </mo> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>: (<b>a</b>) Azimuth pixel offset is positively related to the difference in the absolute value of squint angles; (<b>b</b>) Azimuth velocity estimation error is negatively related to the difference of the angles.</p>
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<p>Slant range offset caused by range motion among sequential SAR images: (<b>a</b>) With <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>40</mn> <mo> </mo> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and this offset is positively related to deviation of the angles; (<b>b</b>) with squint angle combination <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msup> <mn>5</mn> <mo>°</mo> </msup> <mo>,</mo> <mn>0</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>Gain variation of target caused by EAV. For isotropic target, gain variation is dependent on EAV and independent of squint angles.</p>
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<p>Flowchart of the proposed velocity estimation method.</p>
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<p>Velocity estimation error in both azimuth and range directions with different methods. Azimuth velocity varies from 2 to 30 m/s and range velocity is 30 m/s: (<b>a</b>) Azimuth velocity estimation error; (<b>b</b>) Range velocity estimation error.</p>
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<p>SAR image of squint angle <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mn>1</mn> </msub> <mo>=</mo> <msup> <mn>3</mn> <mo>°</mo> </msup> </mrow> </semantics></math>. Only T3 can be identified and clutter suppression is needed for the other three targets.</p>
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<p>SAR images and ERV results of T1 with squint angle <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mn>1</mn> </msub> <msup> <mrow> <mrow> <mo>=</mo> <mn>3</mn> </mrow> </mrow> <mo>°</mo> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mn>2</mn> </msub> <msup> <mrow> <mrow> <mo>=</mo> <mo>−</mo> <mn>2</mn> </mrow> </mrow> <mo>°</mo> </msup> </mrow> </semantics></math>: (<b>a</b>) SAR images after clutter suppression; (<b>b</b>) ERV results.</p>
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<p>SAR images and ERV results of T2 with squint angle <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mn>1</mn> </msub> <msup> <mrow> <mrow> <mo>=</mo> <mn>3</mn> </mrow> </mrow> <mo>°</mo> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mn>2</mn> </msub> <mo>=</mo> <mo>−</mo> <msup> <mn>2</mn> <mo>°</mo> </msup> </mrow> </semantics></math>: (<b>a</b>) SAR images after clutter suppression; (<b>b</b>) ERV results before refocusing; (<b>c</b>) ERV results after refocusing.</p>
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<p>SAR images and ERV results of T3 with squint angle <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mn>1</mn> </msub> <msup> <mrow> <mrow> <mo>=</mo> <mn>3</mn> </mrow> </mrow> <mo>°</mo> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mn>2</mn> </msub> <msup> <mrow> <mrow> <mo>=</mo> <mo>−</mo> <mn>3</mn> </mrow> </mrow> <mo>°</mo> </msup> </mrow> </semantics></math>: (<b>a</b>) SAR images before clutter suppression; (<b>b</b>) SAR images after clutter suppression; (<b>c</b>) ERV result before refocusing; (<b>d</b>) ERV result after refocusing.</p>
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<p>SAR images and ERV results of T4 with squint angle <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mn>1</mn> </msub> <msup> <mrow> <mrow> <mo>=</mo> <mn>3</mn> </mrow> </mrow> <mo>°</mo> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mn>2</mn> </msub> <msup> <mrow> <mrow> <mo>=</mo> <mo>−</mo> <mn>2</mn> </mrow> </mrow> <mo>°</mo> </msup> </mrow> </semantics></math>: (<b>a</b>) SAR images after clutter suppression; (<b>b</b>) ERV results.</p>
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22 pages, 2490 KiB  
Article
Phase Imbalance Analysis of GF-3 Along-Track InSAR Data for Ocean Current Measurement
by Junxin Yang, Xinzhe Yuan, Bing Han, Liangbo Zhao, Jili Sun, Mingyang Shang, Xiaochen Wang and Chibiao Ding
Remote Sens. 2021, 13(2), 269; https://doi.org/10.3390/rs13020269 - 14 Jan 2021
Cited by 7 | Viewed by 2374 | Correction
Abstract
There are two useful methods of current measurement based on synthetic aperture radar (SAR): one is along-track interferometry (ATI), and the other is Doppler centroid analysis (DCA). For the ATI method, the interferometric phase must be accurate enough for ocean current measurements. Therefore, [...] Read more.
There are two useful methods of current measurement based on synthetic aperture radar (SAR): one is along-track interferometry (ATI), and the other is Doppler centroid analysis (DCA). For the ATI method, the interferometric phase must be accurate enough for ocean current measurements. Therefore, the space-varying of phase imbalances along the range, caused by antenna phase center position error, attitude error, antenna electronic miss pointing, antenna pattern mismatch, and other reasons, cannot be ignored. Firstly, this paper mainly analyzes the above possible factors by using real GF-3 ATI data and error model simulation results. Secondly, the ocean current has been preliminarily measured by the ATI method and the DCA method, using CDOP model, based on the GF-3 ATI data of the ocean scene near Qingdao, China, which is up to around ?1.45 m/s. The results of the two methods are in good agreement with the correlation coefficient of 0.98, the mean difference of ?0.010 m/s, and the root mean squared error (RMSE) of 0.062 m/s. Moreover, by comparing with the current measured by high-frequency surface wave radar (HFSWR), the correctness of the analysis is further proved. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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<p>Geometric model in the azimuth-slant range plane.</p>
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<p>The geometric model with baseline error under the condition of squint imaging.</p>
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<p>Under the condition that the accuracy of radial velocity measurement is 0.1 m/s and the beam code is 162, the allowable range of antenna phase center position error and attitude error is obtained. (<b>a</b>) Allowable antenna phase center position error range: the values of <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>x</mi> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>z</mi> </mrow> </semantics> </math> must be located in the area of diagonal line in the figure. (<b>b</b>) The allowable range of attitude error without considering squint angle: the values of <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mi>θ</mi> <mrow> <mi>y</mi> <mi>a</mi> <mi>w</mi> </mrow> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mi>θ</mi> <mrow> <mi>p</mi> <mi>i</mi> <mi>t</mi> <mi>c</mi> <mi>h</mi> </mrow> </msub> </mrow> </semantics> </math> must be located in the area where the oblique line is drawn in the figure.</p>
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<p>The change of phase imbalance in range caused by maximum attitude control errors of GF-3.</p>
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<p>Phase imbalance calibration flowing diagram.</p>
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<p>The change of <math display="inline"> <semantics> <mrow> <mo>∠</mo> <mi>E</mi> <mo>{</mo> <msubsup> <mi>S</mi> <mn>1</mn> <mo>*</mo> </msubsup> <mrow> <mo>(</mo> <mi>τ</mi> <mo>,</mo> <msub> <mi>f</mi> <mi>a</mi> </msub> <mo>)</mo> </mrow> <mo>·</mo> <msub> <mi>S</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>τ</mi> <mo>,</mo> <msub> <mi>f</mi> <mi>a</mi> </msub> <mo>)</mo> </mrow> <mo>}</mo> </mrow> </semantics> </math> with <math display="inline"> <semantics> <msub> <mi>f</mi> <mi>a</mi> </msub> </semantics> </math>.</p>
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<p>The space-varying of phase imbalances in range. (<b>a</b>) The first set of GF-3 ATI data; (<b>b</b>) the second set of GF-3 ATI data.</p>
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<p>After the dual-channel signal pulse compression of the internal calibration data, the peak position is partially enlarged. (<b>a</b>) The head calibration result of the first set of data; (<b>b</b>) the tail calibration result of the first set of data; (<b>c</b>) the head calibration result of the second of set data; (<b>d</b>) the tail calibration result of the second of set data.</p>
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<p>The relative change of the Doppler centroid in range. (<b>a</b>) The first set of GF-3 ATI data; (<b>b</b>) the second set of GF-3 ATI data.</p>
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<p>The Doppler centroid frequency of the two sets of GF-3 ATI image data of the same channel. (<b>a</b>) The estimated Doppler centroid frequency from real data; (<b>b</b>) the calculated Doppler centroid frequency according to the attitude parameters.</p>
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<p>The relative change of phase imbalances in range after removing the phase imbalance caused by squint imaging. (<b>a</b>) The first set of GF-3 ATI data; (<b>b</b>) the second set of GF-3 ATI data.</p>
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<p>The relative change of phase imbalances in range caused by the attitude error. (<b>a</b>) The first set of GF-3 ATI data; (<b>b</b>) the second set of GF-3 ATI data.</p>
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<p>The phase imbalances of the reference land scene and ocean scene for ocean current inversion. (<b>a</b>) The GF-3 image of the reference land scene; (<b>b</b>) the phase imbalances of the reference land scene; (<b>c</b>) the GF-3 image of the ocean scene; (<b>d</b>) the phase imbalances of the ocean scene.</p>
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<p>GF-3 ATI data procession flow diagram.</p>
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<p>The ocean current retrieved by the ATI method. (<b>a</b>) The radial velocities of the ocean surface; (<b>b</b>) the ocean current velocities.</p>
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<p>The results retrieved by the Doppler centroid analysis (DCA) method. (<b>a</b>) The radial velocities of the ocean surface; (<b>b</b>) the ocean current velocities.</p>
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<p>Statistical chart of the error distribution of the ocean current obtained by the two methods.</p>
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20 pages, 5648 KiB  
Article
Improved Method to Suppress Azimuth Ambiguity for Current Velocity Measurement in Coastal Waters Based on ATI-SAR Systems
by Na Yi, Yijun He and Baochang Liu
Remote Sens. 2020, 12(20), 3288; https://doi.org/10.3390/rs12203288 - 10 Oct 2020
Cited by 5 | Viewed by 2579
Abstract
Measurements of ocean surface currents in coastal waters are crucial for improving our understanding of tidal atlases, as well as for ecosystem and water pollution monitoring. This paper proposes an improved method for estimating the baseline-to-platform speed ratio (BPSR) for improving the current [...] Read more.
Measurements of ocean surface currents in coastal waters are crucial for improving our understanding of tidal atlases, as well as for ecosystem and water pollution monitoring. This paper proposes an improved method for estimating the baseline-to-platform speed ratio (BPSR) for improving the current line-of-sight (LOS) velocity measurement accuracy in coastal waters with along-track interferometric synthetic aperture radar (ATI-SAR) based on eigenvalue spectrum entropy (EVSE) analysis. The estimation of BPSR utilizes the spaceborne along-track interferometry and considers the effects of a satellite orbit and an inaccurate baseline responsible for azimuth ambiguity in coastal waters. Unlike the existing methods, which often assume idealized rather than actual operating environments, the proposed approach considers the accuracy of BPSR, which is its key advantage applicable to many, even poorly designed, ATI-SAR systems. This is achieved through an alternate algorithm for the suppression of azimuth ambiguity and BPSR estimation based on an improved analysis of the eigenvalue spectrum entropy, which is an important parameter representing the mixability of unambiguous and ambiguous signals. The improvements include the consideration of a measurement of the heterogeneity of the scene, the corrections of coherence-inferred phase fluctuation (CPF), and the interferogram-derived phase variability (IPV); the last two variables are closely related to the determination of the EVSE threshold. Besides, the BPSR estimation also represents an improvement that has not been achieved in previous work of EVSE analysis. When the improved method is used on the simulated ocean-surface current LOS velocity data obtained from a coastal area, the root-mean-square error is less than 0.05 m/s. The other strengths of the proposed algorithm are adaptability, robustness, and a limited user input requirement. Most importantly, the method can be adopted for practical applications. Full article
(This article belongs to the Special Issue Coastal Waters Monitoring Using Remote Sensing Technology)
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<p>Illustration of the Doppler amplitude patterns of the two azimuth ambiguities and the unambiguous signal part. Modified from Liu [<a href="#B31-remotesensing-12-03288" class="html-bibr">31</a>].</p>
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<p>Flowchart of the proposed approach.</p>
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<p>Flowchart of the alternate iterative algorithm.</p>
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<p>Interferometric phase trend after Doppler bin removal based on the two maximum points of the eigenvalue spectrum entropy (EVSE) curve from [<a href="#B31-remotesensing-12-03288" class="html-bibr">31</a>].</p>
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<p>(<b>a</b>) Azimuth ambiguity of the SAR image in the coastal area (note the three bright objects in the land area and their ghost signatures in the ocean area); (<b>b</b>) Interferogram phase image.</p>
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<p>Interferogram amplitude image sampled of the region marked by the rectangle.</p>
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<p>Doppler interval endpoint curves after several iterations using the simulated data (the red line indicates the terminal point of the Doppler range, and the blue line indicates the starting point of the Doppler range).</p>
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<p>BPSR estimation using simulated data and the proposed algorithm.</p>
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<p>(<b>a</b>) SAR image after azimuth ambiguity suppression; (<b>b</b>) interferogram phase image after azimuth ambiguity suppression.</p>
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<p>(<b>a</b>) Phase-frequency curve comparison in the first iteration of ambiguity suppression using simulated data (the blue line is original interferometric phase trend, and the red line is the phase trend after azimuth ambiguity suppression); (<b>b</b>) the phase-frequency curve comparison at the final iteration of ambiguity suppression using simulated data.</p>
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<p>Retrieved horizontal LOS (line-of-sight) current Doppler velocity field based on the simulation data with a true current velocity of 3 m/s. (<b>a</b>) is without the algorithm application. (<b>b</b>) processed with the improved algorithm.</p>
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<p>Range-compressed azimuth-unfocused SAR image.</p>
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<p>Doppler interval endpoint curve after several iterations using measured data (the red line is the terminal point and the blue line is the starting point of the Doppler range).</p>
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<p>BPSR estimate using measured data and the proposed algorithm.</p>
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<p>Phase-frequency curve comparison without consideration of scene heterogeneity.</p>
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<p>Phase-frequency curve comparison in the first iteration of ambiguity suppression using the measured data; (<b>b</b>) the phase-frequency curve comparison in the final iteration of ambiguity suppression using the measure data.</p>
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22 pages, 3265 KiB  
Article
COSMO-SkyMed Staring Spotlight SAR Data for Micro-Motion and Inclination Angle Estimation of Ships by Pixel Tracking and Convex Optimization
by Biondi Filippo
Remote Sens. 2019, 11(7), 766; https://doi.org/10.3390/rs11070766 - 29 Mar 2019
Cited by 22 | Viewed by 5425
Abstract
In past research, the problem of maritime targets detection and motion parameter estimation has been tackled. This new research aims to contribute by estimating the micro-motion of ships while they are anchored in port or stationed at the roadstead for logistic operations. The [...] Read more.
In past research, the problem of maritime targets detection and motion parameter estimation has been tackled. This new research aims to contribute by estimating the micro-motion of ships while they are anchored in port or stationed at the roadstead for logistic operations. The problem of motion detection of targets is solved using along-track interferometry (ATI) which is observed using two radars spatially distanced by a baseline extended in the azimuth direction. In the case of spaceborne missions, the performing of ATI requests using at least two real-time SAR observations spatially distanced by an along-track baseline. For spotlight spaceborne SAR re-synthesizing two ATI observations from one raw data is a problem because the received electromagnetic bursts are not oversampled for onboard memory space saving and data appears like a white random process. This problem makes appearing interlaced Doppler bands completely disjointed. This phenomenon, after the range-Doppler focusing process, causes decorrelation when considering the ATI interferometric phase information retransmitted by distributed targets. Only small and very coherent targets located within the same radar resolution cell are considered. This paper is proposing a new approach where the micro-motion estimation of ships, occupying thousands of pixels, is measured processing the information given by sub-pixel tracking generated during the coregistration process of two re-synthesized time-domain and partially overlapped sub-apertures generated splitting the raw data observed by a single wide Doppler band staring spotlight (ST) SAR map. The inclination of ships is calculated by low-rank plus sparse decomposition and Radon transform of some region of interest. Experiments are performed processing one set of COSMO-SkyMed ST SAR data. Full article
(This article belongs to the Special Issue Pattern Analysis and Recognition in Remote Sensing)
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<p>SAR acquisition geometry.</p>
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<p>(<b>a</b>): White random process. (<b>b</b>): CSK raw data of a Doppler line.</p>
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<p>(<b>a</b>): Particular of the white random process. (<b>b</b>): CSK raw data of a raw Doppler line.</p>
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<p>(<b>a</b>): Spectrum of the white random process. (<b>b</b>): spectrum of the CSK raw Doppler line.</p>
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<p>Computational scheme for features extraction.</p>
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<p>(<b>a</b>): SLC ROI. (<b>b</b>): Magnitude and phase displacement profile.</p>
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<p>(<b>a</b>): Infra-chromatic coherence ROI (magnitude). (<b>b</b>): Infra-chromatic coherence ROI (phase).</p>
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<p>(<b>a</b>): Magnitude of the range-azimuth displacement. (<b>b</b>): Phase of the range-azimuth displacement.</p>
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<p>(<b>a</b>): RT of the displacement ROI. (<b>b</b>): Sparse component of the RT of the displacement ROI.</p>
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<p>(<b>a</b>): RT of the displacement ROI (Particular 1 of <a href="#remotesensing-11-00766-f009" class="html-fig">Figure 9</a>). (<b>b</b>): Sparse component of the RT of the displacement ROI (Particular 2 of <a href="#remotesensing-11-00766-f009" class="html-fig">Figure 9</a>).</p>
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<p>(<b>a</b>): Reflectivity profiles. (<b>b</b>): Receiving operating characteristic (ROC) curve.</p>
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<p>(<b>a</b>): RT of the displacement ROI (Particular 4 of <a href="#remotesensing-11-00766-f009" class="html-fig">Figure 9</a>). (<b>b</b>): Sparse component of the RT of the displacement ROI (Particular 3 of <a href="#remotesensing-11-00766-f009" class="html-fig">Figure 9</a>).</p>
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<p>(<b>a</b>): Reflectivity profiles. (<b>b</b>): Receiving operating characteristic (ROC) curve.</p>
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<p>Complex velocity field.</p>
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<p>(<b>a</b>): Magnitude of the SLC ROI. (<b>b</b>): ROI velocity field (magnitude).</p>
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<p>(<b>a</b>): Phase component of the range-azimuth displacement. (<b>b</b>): Magnitude and phase displacement profile.</p>
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<p>(<b>a</b>): RT of the displacement ROI. (<b>b</b>): Sparse component of the RT of the displacement ROI.</p>
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<p>(<b>a</b>): RT of the displacement ROI (Particular 1 of <a href="#remotesensing-11-00766-f017" class="html-fig">Figure 17</a>). (<b>b</b>): Sparse component of the RT of the displacement ROI (Particular 4 of <a href="#remotesensing-11-00766-f017" class="html-fig">Figure 17</a>).</p>
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<p>(<b>a</b>): Reflectivity profiles. (<b>b</b>): Receiving operating characteristic (ROC) curve.</p>
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<p>(<b>a</b>): RT of the displacement ROI (Particular 2 of <a href="#remotesensing-11-00766-f017" class="html-fig">Figure 17</a>). (<b>b</b>): Sparse component of the RT of the displacement ROI (Particular 3 of <a href="#remotesensing-11-00766-f017" class="html-fig">Figure 17</a>).</p>
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<p>(<b>a</b>): Reflectivity profiles. (<b>b</b>): Receiving operating characteristic (ROC) curve.</p>
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<p>Complex velocity field.</p>
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<p>(<b>a</b>): Study case number 1 target angles estimation. (<b>b</b>):Study case number 2 target angles estimation.</p>
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<p>(<b>a</b>): Large ship SAR image. (<b>b</b>): Small ship SAR image.</p>
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<p>(<b>a</b>): Large ship displacement map (magnitude). (<b>b</b>): Large ship displacement map (phase).</p>
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<p>(<b>a</b>): Small ship displacement map (magnitude). (<b>b</b>): Small ship displacement map (phase).</p>
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2908 KiB  
Article
GMTI for Squint Looking XTI-SAR with Rotatable Forward-Looking Array
by Kai Jing, Jia Xu, Zuzhen Huang, Di Yao and Teng Long
Sensors 2016, 16(6), 873; https://doi.org/10.3390/s16060873 - 14 Jun 2016
Cited by 4 | Viewed by 6858
Abstract
To realize ground moving target indication (GMTI) for a forward-looking array, we propose a novel synthetic aperture radar (SAR) system, called rotatable cross-track interferometry SAR (Ro-XTI-SAR), for squint-looking application in this paper. By changing the angle of the cross-track baseline, the interferometry phase [...] Read more.
To realize ground moving target indication (GMTI) for a forward-looking array, we propose a novel synthetic aperture radar (SAR) system, called rotatable cross-track interferometry SAR (Ro-XTI-SAR), for squint-looking application in this paper. By changing the angle of the cross-track baseline, the interferometry phase component of squint-looking Ro-XTI-SAR caused by the terrain height can be approximately adjusted to zero, and then the interferometry phase of Ro-XTI-SAR is only sensitive to targets’ motion and can be equivalent to the along track interferometry SAR (ATI-SAR). Furthermore, the conventional displaced phase center array (DPCA) method and constant false alarm (CFAR) processing can be used to accomplish the successive clutter suppression, moving targets detection and relocation. Furthermore, the clutter suppressing performance is discussed with respect to different system parameters. Finally, some results of numerical experiments are provided to demonstrate the effectiveness of the proposed system. Full article
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<p>Side looking airborne ATI-SAR system geometry model.</p>
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<p>Airborne squint looking XTI-SAR system.</p>
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<p>Squint-looking XTI-SAR interferometry phases of different baseline angles. (<b>a</b>) When <span class="html-italic">h</span> = 50 m, <span class="html-italic">v<sub>r</sub></span> = 1 m/s and <math display="inline"> <semantics> <mi>θ</mi> </semantics> </math> = 45° the interferometry phase of static target is forced zero when <span class="html-italic">β</span> = 135°, the interferometry phase with respect to target’s height is non-zero and could be used to estimate the velocity; (<b>b</b>) It is demonstrated that the interferometry phase <math display="inline"> <semantics> <mrow> <msub> <mi>ϕ</mi> <mrow> <mtext>XTI</mtext> </mrow> </msub> </mrow> </semantics> </math> caused by the static targets with different height <span class="html-italic">h</span> are all forced to zeros when <math display="inline"> <semantics> <mi>β</mi> </semantics> </math> = 180 − <math display="inline"> <semantics> <mi>θ</mi> </semantics> </math>; (<b>c</b>) When <math display="inline"> <semantics> <mi>θ</mi> </semantics> </math> = 45° and <math display="inline"> <semantics> <mi>β</mi> </semantics> </math> = 45°, <span class="html-italic">i.e.</span>, <math display="inline"> <semantics> <mrow> <mi>sin</mi> <mo stretchy="false">(</mo> <mi>θ</mi> <mo>+</mo> <mi>β</mi> <mo stretchy="false">)</mo> <mo>≠</mo> <mn>0</mn> </mrow> </semantics> </math>, the interferometry phase <math display="inline"> <semantics> <mrow> <msub> <mi>ϕ</mi> <mrow> <mtext>XTI</mtext> </mrow> </msub> </mrow> </semantics> </math> is jointly decided by terrain height and target radial velocity; (<b>d</b>) When <math display="inline"> <semantics> <mi>θ</mi> </semantics> </math> = 45° and <math display="inline"> <semantics> <mi>β</mi> </semantics> </math> = 135°, <span class="html-italic">i.e.</span>, <math display="inline"> <semantics> <mrow> <mi>sin</mi> <mo stretchy="false">(</mo> <mi>θ</mi> <mo>+</mo> <mi>β</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math>, the interferometry phase <math display="inline"> <semantics> <mrow> <msub> <mi>ϕ</mi> <mrow> <mtext>XTI</mtext> </mrow> </msub> </mrow> </semantics> </math> is constant regardless of height <span class="html-italic">h</span> and it is linearly varied with target’s radial velocity <span class="html-italic">v</span><sub>r</sub>. That is, the interferometry phase will be linearly related to the target’s radial velocity <span class="html-italic">v</span><sub>r</sub>.</p>
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<p>Ro-XTI-SAR system model with the rotatable forward-looking array.</p>
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<p>The signal processing flowchart of a three-channel Ro-XTI-SAR system.</p>
Full article ">Figure 6
<p>The suppression factor <span class="html-italic">versus</span> different system parameters. (<b>a</b>) is the suppression factor versus incidence angle difference when the target height is 100m and squint angle is 15°; (<b>b</b>) is the suppression factor versus incidence angle difference when the target height is 100m and squint angle is 60°; (<b>c</b>,<b>d</b>) are the suppression factor versus the incidence angle at different targets’ height. It’s found that the suppression factor becomes better with the increases of the incidence angle accordingly.</p>
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<p>Ro-XTI-SAR system geometry and simulated targets.</p>
Full article ">Figure 8
<p>GMTI results of three-channel airborne Ro-XTI-SAR system. (<b>a</b>) is the previous SAR image; (<b>b</b>) is the image after DPCA of a traditional XTI-SAR with the baseline angle 90°; (<b>c</b>) is the image after DPCA of the proposed Ro-XTI-SAR system; (<b>d</b>) is the detection and relocation results of the moving targets.</p>
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