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20 pages, 8725 KiB

Open AccessArticle

High-Resolution Azimuth Missing Data SAR Imaging Based on Sparse Representation Autofocusing

by Nan Jiang, Huagui Du, Shaodi Ge, Jiahua Zhu, Dong Feng, Jian Wang and Xiaotao Huang

Remote Sens. 2023, 15(13), 3425; https://doi.org/10.3390/rs15133425 - 06 Jul 2023

Cited by 2 | Viewed by 906

Due to significant electromagnetic interference, radar interruptions, and other factors, Azimuth Missing Data (AMD) may occur in Synthetic Aperture Radar (SAR) echo, resulting in severe defocusing and even false targets. An important approach to solving this problem is to utilize Compressed Sensing (CS) [...] Read more.

Due to significant electromagnetic interference, radar interruptions, and other factors, Azimuth Missing Data (AMD) may occur in Synthetic Aperture Radar (SAR) echo, resulting in severe defocusing and even false targets. An important approach to solving this problem is to utilize Compressed Sensing (CS) methods on AMD echo to reconstruct complete echo, which can be abbreviated as the AMD Imaging Algorithm (AMDIA). However, the State-of-the-Art AMDIA (SOA-AMDIA) do not consider the influence of motion phase errors, resulting in an unacceptable estimation error of the complete echo reconstruction. Therefore, in order to enhance the practical applicability of AMDIA, this article proposes an improved AMDIA using Sparse Representation Autofocusing (SRA-AMDIA). The proposed SRA-AMDIA aims to accurately focus the imaging result, even in the Phase Error AMD (PE-AMD) echo case. Firstly, a Phase-Compensation Function (PCF) based on the phase history of the scene centroid is designed. When the PCF is multiplied with the PE-AMD echo in the range-frequency domain, a coarse-focused sparse representation signal can be obtained in the range-Doppler domain. However, due to the influence of unknown PE, the sparsity of this sparse representation signal is unsatisfying, breaking the sparse constraints requirement of the CS method. Therefore, we introduced a minimum entropy autofocusing algorithm to autofocus this sparse representation signal. Next, the estimated PE is compensated for this sparse representation signal, and a more sparse representation signal is obtained. Hence, the non-PE complete echo can be reconstructed. Finally, the estimated complete echo can be used with classic imaging algorithms to obtain high-resolution imaging results under the PE-AMD condition. Simulation and real measured data have verified the effectiveness of the proposed SRA-AMDIA. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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21 pages, 7267 KiB

Open AccessArticle

Static High Target-Induced False Alarm Suppression in Circular Synthetic Aperture Radar Moving Target Detection Based on Trajectory Features

by Wenjie Shen, Fan Ding, Yanping Wang, Yang Li, Jinping Sun, Yun Lin, Wen Jiang and Shuo Wang

Remote Sens. 2023, 15(12), 3164; https://doi.org/10.3390/rs15123164 - 18 Jun 2023

Viewed by 1013

Abstract

The new mode of Circular Synthetic Aperture Radar (CSAR) has several advantages including multi-aspect and long-time observation, which can generate high-frame-rate image sequences to detect moving targets with a single-channel system. Nonetheless, due to CSAR being sensitive to 3D structures, static high targets [...] Read more.

The new mode of Circular Synthetic Aperture Radar (CSAR) has several advantages including multi-aspect and long-time observation, which can generate high-frame-rate image sequences to detect moving targets with a single-channel system. Nonetheless, due to CSAR being sensitive to 3D structures, static high targets are observed in scene display rotational motion within CSAR subaperture image sequences. Such motion can cause false alarms rising when utilizing image sequence-based moving target detection methods like logarithm background subtraction (LBS). To address this issue, this paper first thoroughly analyzes the moving target and static high target’s difference for the trajectory in an image sequence. Two new trajectory features of the rotation angle and moving distance are proposed to differentiate them. Based on the features, a new false alarm suppression method is proposed. The method first utilizes LBS to obtain coarse binary detection results comprising both moving and static high targets, then employs morphological filtering to eliminate noise. Next, DBSCAN and target tracking steps are employed to extract the trajectory features of the target and false alarm. Finally, false alarms are suppressed with trajectory-based feature discriminators to output detection results. The W-band CSAR open dataset is used to validate the proposed method’s effectiveness. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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19 pages, 33540 KiB

Open AccessArticle

The Calibration Method of Multi-Channel Spatially Varying Amplitude-Phase Inconsistency Errors in Airborne Array TomoSAR

by Dawei Wang, Fubo Zhang, Longyong Chen, Zhenhua Li and Ling Yang

Remote Sens. 2023, 15(12), 3032; https://doi.org/10.3390/rs15123032 - 09 Jun 2023

Cited by 1 | Viewed by 1008

Abstract

Airborne array tomographic synthetic aperture radar (TomoSAR) can acquire three-dimensional (3D) information of the observed scene in a single pass. In the process of airborne array TomoSAR data imaging, due to the disturbance of factors such as inconsistent antenna patterns and baseline errors, [...] Read more.

Airborne array tomographic synthetic aperture radar (TomoSAR) can acquire three-dimensional (3D) information of the observed scene in a single pass. In the process of airborne array TomoSAR data imaging, due to the disturbance of factors such as inconsistent antenna patterns and baseline errors, there are spatially varying amplitude-phase inconsistency errors in the multi-channel Single-Look-Complex (SLC) images. The existence of the errors degrades the quality of the 3D imaging results, which suffer from positioning errors, stray points, and spurious targets. In this paper, a new calibration method based on multiple prominent points is proposed to calibrate the errors of amplitude-phase inconsistency. Firstly, the prominent points are selected from the multi-channel SLC data. Then, the subspace decomposition method and maximum interference spectrum method are used to extract the multi-channel amplitude-phase inconsistency information at each point. The last step is to fit the varying curve and to compensate for the errors. The performance of the method is verified using actual data. The experimental results show that compared with the traditional fixed amplitude-phase inconsistency calibration method, the proposed method can effectively calibrate spatially varying amplitude-phase inconsistency errors, thus improving on the accuracy of 3D reconstruction results for large-scale scenes. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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28 pages, 36592 KiB

Open AccessArticle

An Interferogram Re-Flattening Method for InSAR Based on Local Residual Fringe Removal and Adaptively Adjusted Windows

by Di Zhuang, Lamei Zhang and Bin Zou

Remote Sens. 2023, 15(8), 2214; https://doi.org/10.3390/rs15082214 - 21 Apr 2023

Cited by 1 | Viewed by 1176

Abstract

InSAR technology uses the geometry between antennas and targets to obtain DEM and deformation; therefore, accurate orbit information, which can provide reliable geometry, is the prerequisite for InSAR processing. However, the orbit information provided by some satellites may be inaccurate. Further, this inaccuracy [...] Read more.

InSAR technology uses the geometry between antennas and targets to obtain DEM and deformation; therefore, accurate orbit information, which can provide reliable geometry, is the prerequisite for InSAR processing. However, the orbit information provided by some satellites may be inaccurate. Further, this inaccuracy will be reflected in the interferogram and will be difficult to remove, finally resulting in incorrect results. More importantly, it was found that the residual fringes caused by inaccurate orbit information vary unevenly throughout the whole image and cannot be completely removed by the existing refinement and re-flattening methods. Therefore, an interferogram re-flattening method based on local residual fringe removal and adaptively adjusted windows was proposed in this paper, with the aim being to remove the unevenly varying residual fringes. There are two innovative advantages of the proposed method. One advantage is that the method aims at the global inhomogeneity of residual fringes; the idea of combining local processing and residual fringe removal was proposed to ensure the residual fringes in the whole image can be removed. The other is that an adaptively adjusted local flattening window was designed to ensure that the residual fringes within the local window can be removed cleanly. Three sets of GaoFen-3 data and one pair of Sentinle-1A data were used for experiments. The re-flattening process shows that the local flattening and the adjustment of the local window are absolutely essential to the clean removal of time-varying and uneven residual fringes. The generated DEM and the estimated building heights are used to indirectly reflect the performance of re-flattening methods. The final results show that compared with mature refinement and re-flattening methods, the DEMs based on the proposed method are more accurate, which reflects that the proposed method has a better performance in the removal of time-varying and uneven residual fringes. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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24 pages, 22470 KiB

Open AccessArticle

A Polarimetric Scattering Characteristics-Guided Adversarial Learning Approach for Unsupervised PolSAR Image Classification

by Hongwei Dong, Lingyu Si, Wenwen Qiang, Wuxia Miao, Changwen Zheng, Yuquan Wu and Lamei Zhang

Remote Sens. 2023, 15(7), 1782; https://doi.org/10.3390/rs15071782 - 27 Mar 2023

Cited by 2 | Viewed by 1246

Abstract

Highly accurate supervised deep learning-based classifiers for polarimetric synthetic aperture radar (PolSAR) images require large amounts of data with manual annotations. Unfortunately, the complex echo imaging mechanism results in a high labeling cost for PolSAR images. Extracting and transferring knowledge to utilize the [...] Read more.

Highly accurate supervised deep learning-based classifiers for polarimetric synthetic aperture radar (PolSAR) images require large amounts of data with manual annotations. Unfortunately, the complex echo imaging mechanism results in a high labeling cost for PolSAR images. Extracting and transferring knowledge to utilize the existing labeled data to the fullest extent is a viable approach in such circumstances. To this end, we are introducing unsupervised deep adversarial domain adaptation (ADA) into PolSAR image classification for the first time. In contrast to the standard learning paradigm, in this study, the deep learning model is trained on labeled data from a source domain and unlabeled data from a related but distinct target domain. The purpose of this is to extract domain-invariant features and generalize them to the target domain. Although the feature transferability of ADA methods can be ensured through adversarial training to align the feature distributions of source and target domains, improving feature discriminability remains a crucial issue. In this paper, we propose a novel polarimetric scattering characteristics-guided adversarial network (PSCAN) for unsupervised PolSAR image classification. Compared with classical ADA methods, we designed an auxiliary task for PSCAN based on the polarimetric scattering characteristics-guided pseudo-label construction. This approach utilizes the rich information contained in the PolSAR data itself, without the need for expensive manual annotations or complex automatic labeling mechanisms. During the training of PSCAN, the auxiliary task receives category semantic information from pseudo-labels and helps promote the discriminability of the learned domain-invariant features, thereby enabling the model to have a better target prediction function. The effectiveness of the proposed method was demonstrated using data captured with different PolSAR systems in the San Francisco and Qingdao areas. Experimental results show that the proposed method can obtain satisfactory unsupervised classification results. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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26 pages, 8063 KiB

Open AccessArticle

A Novel End-to-End Unsupervised Change Detection Method with Self-Adaptive Superpixel Segmentation for SAR Images

by Linxia Ji, Jinqi Zhao and Zheng Zhao

Remote Sens. 2023, 15(7), 1724; https://doi.org/10.3390/rs15071724 - 23 Mar 2023

Cited by 2 | Viewed by 1713

Abstract

Change detection (CD) methods using synthetic aperture radar (SAR) data have received significant attention in the field of remote sensing Earth observation, which mainly involves knowledge-driven and data-driven approaches. Knowledge-driven CD methods are based on the physical theoretical models with strong interpretability, but [...] Read more.

Change detection (CD) methods using synthetic aperture radar (SAR) data have received significant attention in the field of remote sensing Earth observation, which mainly involves knowledge-driven and data-driven approaches. Knowledge-driven CD methods are based on the physical theoretical models with strong interpretability, but they lack the robust features of being deeply mined. In contrast, data-driven CD methods can extract deep features, but require abundant training samples, which are difficult to obtain for SAR data. To address these limitations, an end-to-end unsupervised CD network based on self-adaptive superpixel segmentation is proposed. Firstly, reliable training samples were selected using an unsupervised pre-task. Then, the superpixel generation and Siamese CD network were integrated into the unified framework to train them end-to-end until the global optimal parameters were obtained. Moreover, the backpropagation of the joint loss function promoted the adaptive adjustment of the superpixel. Finally, the binary change map was obtained. Several public SAR CD datasets were used to verify the effectiveness of the proposed method. The transfer learning experiment was implemented to further explore the ability to detect the changes and generalization performance of our network. The experimental results demonstrate that our proposed method achieved the most competitive results, outperforming seven other advanced deep-learning-based CD methods. Specifically, our method achieved the highest accuracy in OA, F1-score, and Kappa, and also showed superiority in suppressing speckle noise, refining change boundaries, and improving detection accuracy in a small area change. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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22 pages, 6815 KiB

Open AccessArticle

An Operational Processing Framework for Spaceborne SAR Formations

by Naomi Petrushevsky, Andrea Monti Guarnieri, Marco Manzoni, Claudio Prati and Stefano Tebaldini

Remote Sens. 2023, 15(6), 1644; https://doi.org/10.3390/rs15061644 - 18 Mar 2023

Cited by 1 | Viewed by 1330

Abstract

The paper proposes a flexible and efficient wavenumber domain processing scheme suited for close formations of low earth orbiting (LEO) synthetic aperture radar (SAR) sensors hosted on micro-satellites or CubeSats. Such systems aim to generate a high-resolution image by combining data acquired by [...] Read more.

The paper proposes a flexible and efficient wavenumber domain processing scheme suited for close formations of low earth orbiting (LEO) synthetic aperture radar (SAR) sensors hosted on micro-satellites or CubeSats. Such systems aim to generate a high-resolution image by combining data acquired by each sensor with a low pulse repetition frequency (PRF). This is usually performed by first merging the different channels in the wavenumber domain, followed by bulk focusing. In this paper, we reverse this paradigm by first upsampling and focusing each acquisition and then combining the focused images to form a high-resolution, unambiguous image. Such a procedure is suited to estimate and mitigate artifacts generated by incorrect positioning of the sensors. An efficient wave–number method is proposed to focus data by adequately coping with the orbit curvature. Two implementations are provided with different quality/efficiency. The image quality in phase preservation, resolution, sidelobes, and ambiguities suppression is evaluated by simulating both point and distributed scatterers. Finally, a demonstration of the capability to compensate for ambiguities due to a small across-track baseline between sensors is provided by simulating a realistic X-band multi-sensor acquisition starting from a stack of COSMO-SkyMed images. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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24 pages, 4349 KiB

Open AccessArticle

Radiometric and Polarimetric Quality Validation of Gaofen-3 over a Five-Year Operation Period

by Le Yang, Lei Shi, Weidong Sun, Jie Yang, Pingxiang Li, Deren Li, Shanwei Liu and Lingli Zhao

Remote Sens. 2023, 15(6), 1605; https://doi.org/10.3390/rs15061605 - 15 Mar 2023

Cited by 5 | Viewed by 1212

Abstract

GaoFen-3 was the first Chinese civilian C-band synthetic aperture radar (SAR) satellite, launched in August 2016. The need for monitoring the satellite’s image quality has been boosted by its widespread applications in various fields. The efficient and scientific assessment of the system’s radiometric [...] Read more.

GaoFen-3 was the first Chinese civilian C-band synthetic aperture radar (SAR) satellite, launched in August 2016. The need for monitoring the satellite’s image quality has been boosted by its widespread applications in various fields. The efficient and scientific assessment of the system’s radiometric and polarimetric performance has been essential in its more than five years of service. The authors collected 90 images of the Inner Mongolia calibration site, 888 images of the Amazon rainforest, and 39,929 images of the Chinese mainland from 2017 to 2021. This was achieved whilst covering the leading imaging modes, such as the spotlight mode, stripmap mode, ultra-fine mode, wave imaging mode, etc. In this study, we derive a framework that incorporates the man-made corner reflectors (CRs) in Mongolia, the traditional Amazon rainforest datasets, and even the long-strip data in the Chinese mainland (known as CRAS) for the purposes of GaoFen-3 radiometric quality analysis and polarimetric validation over its five years of operation. Polarimetric calibration without recourse to the CRs is utilized to measure the polarimetric distortions regardless of the region, and thus requires a higher calibration accuracy for the GaoFen-3 polarimetric monitoring task. Consequently, the modified Quegan method is developed by relaxing the target azimuth symmetry constraint with the Amazon forest datasets. The experiments based on the CRAS demonstrate that the main radiometric characteristics could reach the international level, with an estimated noise-equivalent sigma zero of approximately −30 dB, a radiometric resolution that is better than 2.9 dB, and a single-imagery relative radiation accuracy that is better than 0.51 dB. For polarimetric validation, the modified Quegan method was utilized to measure the crosstalk for quad-pol products to ensure that it was than −40 dB. Meanwhile, non-negligible channel imbalance errors were found in the QPSII and WAV modes, and they were effectively well-calibrated with strip estimators to satisfy the system design. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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18 pages, 16563 KiB

Open AccessArticle

A Reflection Symmetric Target Extraction Method Based on Hypothesis Testing for PolSAR Calibration

by Bowen Chi, Jixian Zhang, Lijun Lu, Shucheng Yang, Guoman Huang and Xu Gao

Remote Sens. 2023, 15(5), 1252; https://doi.org/10.3390/rs15051252 - 24 Feb 2023

Cited by 1 | Viewed by 1067

Abstract

Polarimetric calibration is indispensable to quantitatively apply and analyze the polarimetric synthetic aperture radar (PolSAR) image. At present, the polarimetric calibration methods relying on the assumption of reflection symmetry have been widely used, which need to extract the reference targets that meet the [...] Read more.

Polarimetric calibration is indispensable to quantitatively apply and analyze the polarimetric synthetic aperture radar (PolSAR) image. At present, the polarimetric calibration methods relying on the assumption of reflection symmetry have been widely used, which need to extract the reference targets that meet the assumptions before calibration and then calculate the cross-pol channel imbalance and crosstalk errors. However, the distortion in the uncalibrated image will affect the calculation of polarization features, resulting in inaccurate target extraction results. Consequently, we proposed a reflection symmetric target extraction method that combines with spatial statistics information. The method first takes the initial extraction result based on the polarization power total Span and introduces the hypothesis testing to judge the homogeneous samples. Finally, we automatically calculate the threshold by the Otsu algorithm to achieve high-precision extraction of the reflection symmetric targets. Meanwhile, we carried out the polarimetric calibration experiments based on real C- and X-band airborne PolSAR data and conducted qualitative and quantitative evaluation and analysis of the experimental results. The studies demonstrated that, compared with classical approaches, the proposed approach further improved the accuracy of polarimetric calibration by extracting more accurate reference samples. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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20 pages, 17353 KiB

Open AccessArticle

Study on Ship Kelvin Wake Detection in Numerically Simulated SAR Images

by Jingjing Wang, Lixin Guo, Yiwen Wei and Shuirong Chai

Remote Sens. 2023, 15(4), 1089; https://doi.org/10.3390/rs15041089 - 16 Feb 2023

Cited by 2 | Viewed by 1774

Abstract

To improve ship safety and increase ship concealment, we introduce a nonconvex regularization with a Cauchy-based penalty for discussing the influence of ship parameters and speckle noise in numerically simulated SAR images. First, the Kelvin wake geometry was modeled based on the classic [...] Read more.

To improve ship safety and increase ship concealment, we introduce a nonconvex regularization with a Cauchy-based penalty for discussing the influence of ship parameters and speckle noise in numerically simulated SAR images. First, the Kelvin wake geometry was modeled based on the classic theory of ship wave generation. Second, the scattering echo of the Kelvin wake was calculated using the two-scale method (TSM). Then, using the range-Doppler algorithm (RDA), the scattering echo data obtained by the TSM were processed to obtain the Kelvin wake in SAR images. Finally, the wake was reconstructed in the Radon domain using the Cauchy proximal splitting based on the forward–backward algorithm. The simulation results showed that Kelvin wakes were more easily detected in HH polarization with a large pitch angle and X-band, based on which the influence of ship parameters and speckle noise on the detection of ship wake in numerically simulated SAR images was discussed at different wind speeds. The research conclusions are of value to the development of ship wake stealth technology and the improvement of ship safety. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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26 pages, 55338 KiB

Open AccessArticle

Polarimetric SAR Decomposition Method Based on Modified Rotational Dihedral Model

by Yifan Chen, Lamei Zhang, Bin Zou and Guihua Gu

Remote Sens. 2023, 15(1), 101; https://doi.org/10.3390/rs15010101 - 24 Dec 2022

Cited by 6 | Viewed by 1713

Abstract

Polarimetric decomposition is an effective way to analyze the scattering mechanism of targets in polarimetric synthetic aperture radar (PolSAR) images. However, the analysis of urban areas is frequently a challenge. Most decomposition methods use a rotated dihedral derived via rotation matrix to model [...] Read more.

Polarimetric decomposition is an effective way to analyze the scattering mechanism of targets in polarimetric synthetic aperture radar (PolSAR) images. However, the analysis of urban areas is frequently a challenge. Most decomposition methods use a rotated dihedral derived via rotation matrix to model the double-bounce scattering mechanism of buildings. However, according to electromagnetic theory, the existing dihedral model is not accurate, especially when the orientation angle of the dihedral is large. Therefore, the double-bounce scattering contribution in urban areas with large orientation angles will be difficult to extract. To address this problem, based on physical optics (PO) and geometric optics (GO), the interaction process of electromagnetic waves and the rotational dihedral is analyzed, and then a modified rotational dihedral model (MRDM) is proposed for the accurate representation of the rotational double-bounce scattering mechanism. Accordingly, MRDM is introduced to a five-component decomposition method (MRDM-5SD) to analyze the scattering components in an urban area. The validity of MRDM-5SD is demonstrated using several data sets. The experimental results show that the power contributions of double-bounce scattering in urban areas with large orientation angles increase by using MRDM-5SD. Therefore, MRDM can provide support for feature extraction and target detection in urban areas. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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16 pages, 34159 KiB

Open AccessTechnical Note

Radiometric Terrain Correction Method Based on RPC Model for Polarimetric SAR Data

by Lei Zhao, Erxue Chen, Zengyuan Li, Yaxiong Fan and Kunpeng Xu

Remote Sens. 2023, 15(7), 1909; https://doi.org/10.3390/rs15071909 - 02 Apr 2023

Cited by 1 | Viewed by 1736

Abstract

Radiometric terrain correction (RTC) is an important preprocessing step for synthetic aperture radar (SAR) data application in mountainous areas. At present, the RTC processing of SAR depends on the Range Doppler (RD) positioning model. However, the solution of this model has a high [...] Read more.

Radiometric terrain correction (RTC) is an important preprocessing step for synthetic aperture radar (SAR) data application in mountainous areas. At present, the RTC processing of SAR depends on the Range Doppler (RD) positioning model. However, the solution of this model has a high threshold for ordinary remote sensing technicians. To solve this problem, we propose an RTC method based on the rational polynomial coefficient (RPC) model, which is widely used in optical remote sensing and is simpler and more practical than the RD model. China’s GF-3 polarimetric SAR data were used to verify the proposed method. The experimental results showed that the RTC method based on RPC is effective and can achieve better correction effects on the premise of reducing the complexity of the algorithm. The correction effect based on the RPC model can be similar to that based on the RD model. The proposed approach can realize the correction of 4~5 dB terrain radiation distortion to a 0.5 dB level. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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15 pages, 1320 KiB

Open AccessTechnical Note

Radiometric Re-Compensation of Sentinel-1 SAR Data Products for Artificial Biases due to Antenna Pattern Changes

by Kersten Schmidt, Marco Schwerdt, Guillaume Hajduch, Pauline Vincent, Andrea Recchia and Muriel Pinheiro

Remote Sens. 2023, 15(5), 1377; https://doi.org/10.3390/rs15051377 - 28 Feb 2023

Cited by 2 | Viewed by 1484

Abstract

SAR data products for Sentinel-1 have been freely available and delivered operationally on behalf of the European Space Agency since the routine operation of Sentinel-1A in 2014. These products were delivered using the best knowledge at their processing time, in particular with respect [...] Read more.

SAR data products for Sentinel-1 have been freely available and delivered operationally on behalf of the European Space Agency since the routine operation of Sentinel-1A in 2014. These products were delivered using the best knowledge at their processing time, in particular with respect to the radiometric calibration. As reprocessing of SAR data products is not foreseen in the nominal processing chain of Sentinel-1, changes of applied processing parameters impact the SAR data quality and can be a disturbing factor for long-term monitoring of radiometric features. In particular, antenna pattern updates produce artificial radiometric steps which are visible in radar backscatter time series, especially in case of monitoring radiometric stable reference targets. This paper introduces a method for correcting changes due to such updates without the need of reprocessing SAR data products. The method was applied to long-lasting time series of data acquisitions which are used to monitor the radiometric performance with reference targets at the DLR calibration site. It has been shown that artificial steps due to antenna pattern updates disappear in backscatter timelines after correct application. Furthermore, the derived absolute radiometric accuracy was improved for the joint observation period of S1A and S1B for almost five years until December 2021. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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17 pages, 5526 KiB

Open AccessTechnical Note

An Accelerated Hybrid Method for Electromagnetic Scattering of a Composite Target–Ground Model and Its Spotlight SAR Image

by Juan Li, Wei Meng, Shuirong Chai, Lixin Guo, Yongji Xi, Shunkang Wen and Ke Li

Remote Sens. 2022, 14(24), 6332; https://doi.org/10.3390/rs14246332 - 14 Dec 2022

Cited by 3 | Viewed by 1144

Abstract

In this paper, an accelerated hybrid method of physical optics (PO) shooting and bouncing ray (SBR)–physical theory of diffraction (PTD) is proposed to deal with the electromagnetic scattering of a complex target on rough ground. To accelerate the ray tracing progress, the ray [...] Read more.

In this paper, an accelerated hybrid method of physical optics (PO) shooting and bouncing ray (SBR)–physical theory of diffraction (PTD) is proposed to deal with the electromagnetic scattering of a complex target on rough ground. To accelerate the ray tracing progress, the ray marching technique based on octree structure is employed. In this technique, only the nodes passed by the ray are detected successively until the first facet intersected by ray is found or the ray passes through the bounding box, which greatly decreases the intersection test. Then, based on the accelerated PO-SBR-PTD method, the spotlight synthetic aperture radar (SAR) echo data of the composite target–ground model is obtained by the vector superposition of the echo on each meshed patch. Furthermore, the spotlight SAR image of the composite model is simulated by the polar format algorithm (PFA). In numerical simulations, both the EM scattering of the target and composite model are calculated and evaluated by comparing with the multilevel fast multipole method (MLFMM) in FEKO software. Meanwhile the spotlight SAR image of the composite target–ground model is also compared with the real image in MSTAR data, and a satisfactory similarity between them is obtained. In addition, the SAR images of two targets on rough ground for different pose angles are also presented and analyzed. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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