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23 pages, 2216 KiB

Open AccessArticle

Complex-Valued 2D-3D Hybrid Convolutional Neural Network with Attention Mechanism for PolSAR Image Classification

by Wenmei Li, Hao Xia, Jiadong Zhang, Yu Wang, Yan Jia and Yuhong He

Remote Sens. 2024, 16(16), 2908; https://doi.org/10.3390/rs16162908 - 9 Aug 2024

Cited by 2 | Viewed by 1141

Abstract

The recently introduced complex-valued convolutional neural network (CV-CNN) has shown considerable advancements for polarimetric synthetic aperture radar (PolSAR) image classification by effectively incorporating both magnitude and phase information. However, a solitary 2D or 3D CNN encounters challenges such as insufficiently extracting scattering channel [...] Read more.

The recently introduced complex-valued convolutional neural network (CV-CNN) has shown considerable advancements for polarimetric synthetic aperture radar (PolSAR) image classification by effectively incorporating both magnitude and phase information. However, a solitary 2D or 3D CNN encounters challenges such as insufficiently extracting scattering channel dimension features or excessive computational parameters. Moreover, these networks’ default is that all information is equally important, consuming vast resources for processing useless information. To address these issues, this study presents a new hybrid CV-CNN with the attention mechanism (CV-2D/3D-CNN-AM) to classify PolSAR ground objects, possessing both excellent computational efficiency and feature extraction capability. In the proposed framework, multi-level discriminative features are extracted from preprocessed data through hybrid networks in the complex domain, along with a special attention block to filter the feature importance from both spatial and channel dimensions. Experimental results performed on three PolSAR datasets demonstrate our present approach’s superiority over other existing ones. Furthermore, ablation experiments confirm the validity of each module, highlighting our model’s robustness and effectiveness. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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19 pages, 15821 KiB

Open AccessArticle

A Novel Multi-Beam SAR Two-Dimensional Ambiguity Suppression Method Based on Azimuth Phase Coding

by Yihao Xu, Fubo Zhang, Wenjie Li, Yangliang Wan, Longyong Chen and Tao Jiang

Remote Sens. 2024, 16(13), 2298; https://doi.org/10.3390/rs16132298 - 24 Jun 2024

Viewed by 657

Abstract

In order to address the problems of range ambiguity and azimuth ambiguity in the wide-swath imaging of synthetic aperture radar (SAR), this paper proposes a multi-beam SAR two-dimensional ambiguity suppression method based on azimuth phase coding (APC). The scheme employs an elevation simultaneous [...] Read more.

In order to address the problems of range ambiguity and azimuth ambiguity in the wide-swath imaging of synthetic aperture radar (SAR), this paper proposes a multi-beam SAR two-dimensional ambiguity suppression method based on azimuth phase coding (APC). The scheme employs an elevation simultaneous multi-beam transmission system with azimuth under-sampling, transmitting different APC waveforms to various range-ambiguous sub-regions. After receiving the echoes, the azimuth digital beamforming (DBF) is used to separate the APC waveform echoes with multi-order Doppler ambiguity, achieving azimuth reconstruction and range ambiguity suppression simultaneously. Finally, the elevation nulling DBF is used to further suppress range ambiguity and obtain the SAR wide-swath image. The superiority of this scheme is reflected in the following aspects: the azimuth DBF simultaneously suppresses azimuth and range ambiguity, the influence of height fluctuations on the ability to suppress range ambiguity is weakened, the use of elevation nulling DBF further enhances the level of range ambiguity suppression, and different range sub-regions can adopt different range resolutions and working modes. The feasibility of this scheme is verified through theoretical analysis and simulation. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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16 pages, 3250 KiB

Open AccessArticle

Iterative Adaptive Based Multi-Polarimetric SAR Tomography of the Forested Areas

by Shuang Jin, Hui Bi, Qian Guo, Jingjing Zhang and Wen Hong

Remote Sens. 2024, 16(9), 1605; https://doi.org/10.3390/rs16091605 - 30 Apr 2024

Cited by 1 | Viewed by 1079

Abstract

Synthetic aperture radar tomography (TomoSAR) is an extension of synthetic aperture radar (SAR) imaging. It introduces the synthetic aperture principle into the elevation direction to achieve three-dimensional (3-D) reconstruction of the observed target. Compressive sensing (CS) is a favorable technology for sparse elevation [...] Read more.

Synthetic aperture radar tomography (TomoSAR) is an extension of synthetic aperture radar (SAR) imaging. It introduces the synthetic aperture principle into the elevation direction to achieve three-dimensional (3-D) reconstruction of the observed target. Compressive sensing (CS) is a favorable technology for sparse elevation recovery. However, for the non-sparse elevation distribution of the forested areas, if CS is selected to reconstruct it, it is necessary to utilize some orthogonal bases to first represent the elevation reflectivity sparsely. The iterative adaptive approach (IAA) is a non-parametric algorithm that enables super-resolution reconstruction with minimal snapshots, eliminates the need for hyperparameter optimization, and requires fewer iterations. This paper introduces IAA to tomographicinversion of the forested areas and proposes a novel multi-polarimetric-channel joint 3-D imaging method. The proposed method relies on the characteristics of the consistent support of the elevation distribution of different polarimetric channels and uses the

L_{2}

-norm to constrain the IAA-based 3-D reconstruction of each polarimetric channel. Compared with typical spectral estimation (SE)-based algorithms, the proposed method suppresses the elevation sidelobes and ambiguity and, hence, improves the quality of the recovered 3-D image. Compared with the wavelet-based CS algorithm, it reduces computational cost and avoids the influence of orthogonal basis selection. In addition, in comparison to the IAA, it demonstrates greater accuracy in identifying the support of the elevation distribution in forested areas. Experimental results based on BioSAR 2008 data are used to validate the proposed method. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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23 pages, 44301 KiB

Open AccessArticle

Synthetic Aperture Ladar Motion Compensation Method Based on Symmetric Triangle Linear Frequency Modulation Continuous Wave Segmented Interference

by Ruihua Shi, Wei Li, Qinghai Dong, Bingnan Wang, Maosheng Xiang and Yinshen Wang

Remote Sens. 2024, 16(5), 793; https://doi.org/10.3390/rs16050793 - 24 Feb 2024

Cited by 1 | Viewed by 924

Abstract

Synthetic Aperture Ladar (SAL) is a sensor that combines laser detection technology with synthetic aperture technology to achieve ultra-high-resolution imaging. Due to its extremely short wavelength, SAL is more sensitive to motion errors. The micrometer-level motion will affect the target’s azimuth focus. This [...] Read more.

Synthetic Aperture Ladar (SAL) is a sensor that combines laser detection technology with synthetic aperture technology to achieve ultra-high-resolution imaging. Due to its extremely short wavelength, SAL is more sensitive to motion errors. The micrometer-level motion will affect the target’s azimuth focus. This article proposes an SAL motion compensation method based on Symmetric Triangular Linear Frequency Modulation Continuous Wave (STLFMCW) segmented interference, utilizing the characteristics of a triangular wave, to solve the problem of target azimuth defocusing. This article first establishes an STLFMCW echo signal model based on the SAL system under the influence of motion errors. Secondly, the radial velocity gradient along the azimuth direction is extracted using the triangular-wave-positive and -negative frequency modulation signals segmented interference method. Then, for the initial phase wrapping problem, the frequency spectral cross-correlation method is used to accurately estimate the initial radial velocity error. The radial velocity gradient is integrated along the azimuth to obtain the platform motion trajectory. Finally, the compensation functions are constructed to complete the echo Range Cell Migration (RCM) correction and residual phase compensation, resulting in a focused SAL image. This article verifies the practical effect of this method in eliminating motion errors using only one-period STLFMCW signal through simulation and real experiments. The quantitative results show that compared with the traditional method, the proposed method reduces the azimuth Peak Sidelobe Ratio (PSLR) by 8dB and the Integrated Sidelobe Ratio (ISLR) by 9 dB. This method has significant improvements and is of great significance for high-resolution FMCW SAL imaging. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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24 pages, 13657 KiB

Open AccessArticle

Unmanned Airborne Bistatic Interferometric Synthetic Aperture Radar Data Processing Method Using Bi-Directional Synchronization Chain Signals

by Jinbiao Zhu, Bei Lin, Jie Pan, Yao Cheng, Xiaolan Qiu, Wen Jiang, Yuquan Liu and Mingqian Liu

Remote Sens. 2024, 16(5), 769; https://doi.org/10.3390/rs16050769 - 22 Feb 2024

Viewed by 994

Abstract

The bistatic Interferometric Synthetic Aperture Radar (InSAR) system can overcome the physical limitations imposed by the baseline of monostatic dual-antenna InSAR. It provides greater flexibility and can enhance elevation measurement accuracy through a well-designed baseline configuration. Unmanned aerial vehicles (UAVs) equipped with bistatic [...] Read more.

The bistatic Interferometric Synthetic Aperture Radar (InSAR) system can overcome the physical limitations imposed by the baseline of monostatic dual-antenna InSAR. It provides greater flexibility and can enhance elevation measurement accuracy through a well-designed baseline configuration. Unmanned aerial vehicles (UAVs) equipped with bistatic InSAR, having relatively low cost and high flexibility, are useful for mapping and land resource exploration. However, due to challenges including spatiotemporal synchronization and motion errors, there are limited reports on UAV-borne bistatic InSAR. This paper proposes a comprehensive method for processing data from small UAV-borne bistatic InSAR by integrating two-way synchronization chain signals. The proposed method includes compensation for time and phase synchronization errors, trajectory refinement with synchronized chain and Position and Orientation System (POS) data, high-precision bistatic InSAR imaging, and interferometric processing. Height inversion results based on the proposed method are also provided, which demonstrate the effectiveness of the proposed method in improving the accuracy of interferometric measurement at calibration points from 0.66 m to 0.42 m. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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20 pages, 3088 KiB

Open AccessArticle

Position and Orientation System Error Analysis and Motion Compensation Method Based on Acceleration Information for Circular Synthetic Aperture Radar

by Zhenhua Li, Dawei Wang, Fubo Zhang, Yi Xie, Hang Zhu, Wenjie Li, Yihao Xu and Longyong Chen

Remote Sens. 2024, 16(4), 623; https://doi.org/10.3390/rs16040623 - 7 Feb 2024

Cited by 1 | Viewed by 1053

Abstract

Circular synthetic aperture radar (CSAR) possesses the capability of multi-angle observation, breaking through the geometric observation constraints of traditional strip SAR and holding the potential for three-dimensional imaging. Its sub-wavelength level of planar resolution, resulting from a long synthetic aperture, makes CSAR highly [...] Read more.

Circular synthetic aperture radar (CSAR) possesses the capability of multi-angle observation, breaking through the geometric observation constraints of traditional strip SAR and holding the potential for three-dimensional imaging. Its sub-wavelength level of planar resolution, resulting from a long synthetic aperture, makes CSAR highly valuable in the field of high-precision mapping. However, the motion geometry of CSAR is more intricate compared to traditional strip SAR, demanding high precision from navigation systems. The accumulation of errors over the long synthetic aperture time cannot be overlooked. CSAR exhibits significant coupling between the range and azimuth directions, making traditional motion compensation methods based on linear SAR unsuitable for direct application in CSAR. The dynamic nature of flight, with its continuous changes in attitude, introduces a significant deformation error between the non-rigidly connected Inertial Measurement Unit (IMU) and the Global Positioning System (GPS). This deformation error makes it difficult to accurately obtain radar position information, resulting in imaging defocus. The research in this article uncovers a correlation between the deformation error and radial acceleration. Leveraging this insight, we propose utilizing radial acceleration to estimate residual motion errors. This paper delves into the analysis of Position and Orientation System (POS) errors, presenting a novel high-resolution CSAR motion compensation method based on airborne platform acceleration information. Once the system deformation parameters are calibrated using point targets, the deformation error can be directly calculated and compensated based on the acceleration information, ultimately resulting in the generation of a high-resolution image. In this paper, the effectiveness of the method is verified with airborne flight test data. This method can compensate for the deformation error and effectively improve the peak sidelobe ratio and integral sidelobe ratio of the target, thus improving image quality. The introduction of acceleration information provides new means and methods for high-resolution CSAR imaging. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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18 pages, 2001 KiB

Open AccessArticle

SAR Multi-Angle Observation Method for Multipath Suppression in Enclosed Spaces

by Yun Lin, Jiameng Zhao, Yanping Wang, Yang Li, Wenjie Shen and Zechao Bai

Remote Sens. 2024, 16(4), 621; https://doi.org/10.3390/rs16040621 - 7 Feb 2024

Cited by 2 | Viewed by 1220

Abstract

Synthetic aperture radar (SAR) is a powerful tool for detecting and imaging targets in enclosed environments, such as tunnels and underground garages. However, SAR performance is degraded by multipath effects, which occur when electromagnetic waves are reflected by obstacles, such as walls, and [...] Read more.

Synthetic aperture radar (SAR) is a powerful tool for detecting and imaging targets in enclosed environments, such as tunnels and underground garages. However, SAR performance is degraded by multipath effects, which occur when electromagnetic waves are reflected by obstacles, such as walls, and interfere with the direct signal. This results in the formation of multipath ghost images, which obscure the true target and reduce the image quality. To overcome this challenge, we propose a novel method based on multi-angle observation. This method exploits the fact that the position of ghost images changes depending on the angle of the radar, while the position of the true target remains stable. By collecting and processing multiple data sets from different angles, we can eliminate the ghost images and enhance the target image. In addition, we introduce a center vector distance algorithm to address the complexity and computational intensity of existing multipath suppression algorithms. This algorithm, which defines the primary direction of multi-angle vectors from stable scattering centers as the center vector, processes and synthesizes multiple data sets from multi-angle observations. It calculates the distance of pixel intensity sequences in the composite data image from the center vector. Pixels within a specified threshold are used for imaging, and the final result is obtained. Simulation experiments and real SAR data from underground garages confirm the effectiveness of this method in suppressing multipath ghost images. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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18 pages, 17976 KiB

Open AccessArticle

Studies on High-Resolution Airborne Synthetic Aperture Radar Image Formation with Pseudo-Random Agility of Interpulse Waveform Parameters

by Zheng Ye, Daiyin Zhu, Shilin Niu and Jiming Lv

Remote Sens. 2024, 16(1), 164; https://doi.org/10.3390/rs16010164 - 30 Dec 2023

Cited by 2 | Viewed by 1491

Abstract

By means of alteration of the transmitted linear frequency modulation (LFM) signal waveform parameters, such as pulse width or chirp rate, initial phase, pulse repetition interval (PRI), and chirp rate polarity at every position of synthetic apertures, the pseudo-random agility technology of interpulse [...] Read more.

By means of alteration of the transmitted linear frequency modulation (LFM) signal waveform parameters, such as pulse width or chirp rate, initial phase, pulse repetition interval (PRI), and chirp rate polarity at every position of synthetic apertures, the pseudo-random agility technology of interpulse waveform parameters in airborne Synthetic Aperture Radar (SAR) actively increases the complexity and uncertainty of radar waveforms. This technology confuses jamming interception receivers, thus improving its anti-interference ability for active coherent jamming, which is one of the main research interests of airborne SAR technology. But the pseudo-random agility technology for interpulse waveform parameters faces certain challenges of large computation and complex system design, which need to be further studied and solved. To address these issues, a processing scheme of high-resolution SAR image formation which is appropriate for agile interpulse waveform parameters is proposed in this paper. This method can deal with multiple agile parameters, not only single ones as in most existing literature. Its computation load is nearly comparable to that of traditional SAR image formation with constant waveform parameters. The high-resolution SAR imaging results obtained by processing SAR raw data with agile interpulse waveform parameters demonstrate the effectiveness of the proposed method. In addition, real SAR images with resolutions of 0.5 m and 0.15 m, which are rarely found in the public literature, are shown under the circumstance of randomly changing the transmitted wideband LFM signal pulse parameters one by one. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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20 pages, 10609 KiB

Open AccessArticle

A Modified Iteration-Free SPGA Based on Removing the Linear Phase

by Yi Xie, Yuchen Luan, Longyong Chen and Xin Zhang

Remote Sens. 2023, 15(23), 5535; https://doi.org/10.3390/rs15235535 - 28 Nov 2023

Cited by 1 | Viewed by 887

Abstract

In traditional Stripmap SAR imaging, the platform motion error will bring the phase error in the azimuthal direction to the image, which will have a series of effects on the imaging quality. The traditional autofocus algorithm—Stripmap Phase Gradient Algorithm (SPGA)—can estimate any order [...] Read more.

In traditional Stripmap SAR imaging, the platform motion error will bring the phase error in the azimuthal direction to the image, which will have a series of effects on the imaging quality. The traditional autofocus algorithm—Stripmap Phase Gradient Algorithm (SPGA)—can estimate any order phase error above the second order in theory, but it is difficult to estimate the linear phase error, which leads to the discontinuity of the estimated phase error. It usually needs multiple iterations to focus an image, which is inefficient. Moreover, because the linear phase error cannot be estimated, the traditional SPGA cannot eliminate the target offset in the image, resulting in the distortion of the image in the azimuthal direction. According to the continuity of phase error, we propose a modified iteration-free SPGA based on removing the linear phase. Without iteration, the proposed autofocus algorithm can achieve comparable or even better results than traditional SPGA. In the simulation experiments, piecewise linear errors are added to the images of multiple targets. SPGA still fails to focus the image after six iterations. The average ILSR and ILSR are −7.11 dB and −3.99 dB, respectively, and the average number of point target drift is 8.42 pixels. The proposed algorithm optimizes the average ILSR and ILSR to −12.34 dB and −9.87 dB and reduces the average number of point target drift to 0.16 pixels. In the actual data processing, using image entropy as the evaluation criterion, the time consumption is only 19.25% of SPGA under the condition of achieving the same focusing quality. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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22 pages, 93963 KiB

Open AccessArticle

Spaceborne SAR Time-Series Images Change Detection Based on SAR-SIFT-Logarithm Background Subtraction

by Wenjie Shen, Yunzhen Jia, Yanping Wang, Yun Lin, Yang Li, Zechao Bai and Wen Jiang

Remote Sens. 2023, 15(23), 5533; https://doi.org/10.3390/rs15235533 - 28 Nov 2023

Cited by 1 | Viewed by 1560

Abstract

Synthetic Aperture Radar (SAR) image change detection aims to detect changes with images of the same area acquired at different times. It has wide applications in environmental monitoring, urban planning and resource management. Traditional change detection methods for spaceborne SAR time-series images typically [...] Read more.

Synthetic Aperture Radar (SAR) image change detection aims to detect changes with images of the same area acquired at different times. It has wide applications in environmental monitoring, urban planning and resource management. Traditional change detection methods for spaceborne SAR time-series images typically adopt a pairwise comparison strategy to obtain multi-temporal change information. However, this kind of method has the problem of losing the overall change information, which is time consuming. To address this problem, this paper proposes a new change detection algorithm for spaceborne SAR time-series data based on SAR-SIFT-Logarithm Background Subtraction. This algorithm combines the SAR-SIFT image registration technology with Logarithm Background Subtraction. The method first preprocesses the input time-series data with steps like noise reducing and radiometric calibration. Then, the images will be coregistered by the SAR-SIFT step to avoid mismatches-induced detection performance degradation. Next, the parts that remained unchanged throughout the time period are modeled with a median filter to obtain the static background. The change information is then obtained via the subtraction of background and CFAR detection and clustering. The proposed algorithm is validated using the Sentinel-1 GRD and PAZ-1 time-series dataset. Experimental results demonstrate that the proposed method effectively detects the overall change information and reduces processing time compared to traditional pairwise comparison methods. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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21 pages, 10732 KiB

Open AccessArticle

An Efficient BP Algorithm Based on TSU-ICSI Combined with GPU Parallel Computing

by Ziya Li, Xiaolan Qiu, Jun Yang, Dadi Meng, Lijia Huang and Shujie Song

Remote Sens. 2023, 15(23), 5529; https://doi.org/10.3390/rs15235529 - 27 Nov 2023

Cited by 4 | Viewed by 1348

Abstract

High resolution remains a primary goal in the advancement of synthetic aperture radar (SAR) technology. The backprojection (BP) algorithm, which does not introduce any approximation throughout the imaging process, is broadly applicable and effectively meets the demands for high-resolution imaging. Nonetheless, the BP [...] Read more.

High resolution remains a primary goal in the advancement of synthetic aperture radar (SAR) technology. The backprojection (BP) algorithm, which does not introduce any approximation throughout the imaging process, is broadly applicable and effectively meets the demands for high-resolution imaging. Nonetheless, the BP algorithm necessitates substantial interpolation during point-by-point processing, and the precision and effectiveness of current interpolation methods limit the imaging performance of the BP algorithm. This paper proposes a TSU-ICSI (Time-shift Upsampling-Improved Cubic Spline Interpolation) interpolation method that integrates time-shift upsampling with improved cubic spline interpolation. This method is applied to the BP algorithm and presents an efficient implementation method in conjunction with the GPU architecture. TSU-ICSI not only maintains the accuracy of BP imaging processing but also significantly boosts performance. The effectiveness of the BP algorithm based on TSU-ICSI is confirmed through simulation experiments and by processing measured data collected from both airborne SAR and spaceborne SAR. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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28 pages, 22287 KiB

Open AccessArticle

Deceptive Jamming Algorithm against Synthetic Aperture Radar in Large Squint Angle Mode Based on Non-Linear Chirp Scaling and Low Azimuth Sampling Reconstruction

by Jiaming Dong, Qunying Zhang, Wenqiang Huang, Haiying Wang, Wei Lu and Xiaojun Liu

Remote Sens. 2023, 15(23), 5446; https://doi.org/10.3390/rs15235446 - 21 Nov 2023

Cited by 1 | Viewed by 1447

Abstract

Due to the complex range migration characteristics of large squint angle synthetic aperture radar (SAR), it is difficult for traditional SAR deceptive jamming algorithms to balance focusing ability and computational efficiency. There is an urgent demand for proposing a deceptive jamming algorithm against [...] Read more.

Due to the complex range migration characteristics of large squint angle synthetic aperture radar (SAR), it is difficult for traditional SAR deceptive jamming algorithms to balance focusing ability and computational efficiency. There is an urgent demand for proposing a deceptive jamming algorithm against large squint angle SAR in the field of SAR jamming. This article proposes a deceptive jamming algorithm against SAR with large squint angles based on non-linear chirp scaling and low azimuth sampling reconstruction (NLCSR). The NLCSR algorithm uses a high-order approximation of a high-precision model to accurately construct the jammer’s frequency response (JFR) function. In line with the notion of low azimuth sampling processing of the transformation domain, the construction of the space-variant azimuth modulation phase item is completed using the non-linear chirp scaling method. Compared with the traditional deceptive jamming algorithms against the large squint angle SAR, the NLCSR algorithm only needs Fourier transform and complex multiplication while ensuring the focusing ability, which is easier to implement on an efficient parallel digital signal processor based on fast Fourier transform (FFT). Simulation results prove the superior property of the NLCSR algorithm in focusing ability and computational efficiency. Compared to the existing large squint angle SAR deceptive jamming algorithm, the focusing ability of the NLCSR algorithm is almost the same, and the calculation efficiency is improved by at least 52.1%. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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19 pages, 31842 KiB

Open AccessArticle

Evaluating SAR Radiometric Terrain Correction Products: Analysis-Ready Data for Users

by Africa I. Flores-Anderson, Helen Blue Parache, Vanesa Martin-Arias, Stephanie A. Jiménez, Kelsey Herndon, Stefanie Mehlich, Franz J. Meyer, Shobhit Agarwal, Simon Ilyushchenko, Manoj Agarwal, Andrea Nicolau, Amanda Markert, David Saah and Emil Cherrington

Remote Sens. 2023, 15(21), 5110; https://doi.org/10.3390/rs15215110 - 25 Oct 2023

Cited by 4 | Viewed by 5743

Abstract

Operational applications for Synthetic Aperture Radar (SAR) are under development around the world, driven by the free-and-open access of SAR C-band observations that Sentinel-1 of Copernicus has provided since 2014. Radiometric Terrain Correction (RTC) data are key entry-level products for multiple applications ranging [...] Read more.

Operational applications for Synthetic Aperture Radar (SAR) are under development around the world, driven by the free-and-open access of SAR C-band observations that Sentinel-1 of Copernicus has provided since 2014. Radiometric Terrain Correction (RTC) data are key entry-level products for multiple applications ranging from ecosystem to hazard monitoring. Various open-source software packages exist to create RTC products from Single Look Complex (SLC) or Ground Range Detected (GRD) level SAR data, including the Interferometric SAR Computing Environment (ISCE), and the Sentinel-1 Toolbox from the European Space Agency (SNAP 8). Despite the growing availability of RTC software solutions, little work has been performed to identify differences between RTC products generated using different software packages. This work evaluates several Sentinel-1 RTC products and two other Sentinel-1 Analysis Ready Data (ARD) to address the following questions: (1) Which software provides the most accurate RTC product? and (2) how appropriate for analysis are other non-RTC products that are readily available? The RTCs are produced with GAMMA, ISCE-2, and SNAP 8. The other two ARD products evaluated consisted of an angular-based radiometric slope correction produced in Google Earth Engine (GEE) following Vollrath et al., and the Sentinel-1 GRD product. Products are evaluated across 10 sites in a single image approach for (1) radiometric calibration, (2) geometric corrections, and for (3) geolocation quality. In addition, time-series stacks over two sites representing varied terrain and ecosystems are evaluated. The GAMMA-derived RTC product implemented by the Alaska Satellite Facility (ASF) is used as a reference for some of the time-series metrics. The results provide direct guidance and recommendations about the quality of the RTC and ARD products obtained from open source methods. The results indicate that it is not recommended to use the GRD product with no radiometric or geometric corrections for any applications given low performance in multiple metrics. The radiometric calibration and geometric corrections have overall good performance for all open-source solutions, only the non-RTC products (Vollrath et al. and GRD) portray some significant variances in steep terrain. The geolocation assessment indicated that the GRD product has the most significant displacement errors, followed by SNAP 8 with Digital Elevation Model (DEM) matching, and ISCE-2. RTCs created without DEM-matching performed better for both GAMMA and SNAP 8. The time-series results indicate that SNAP 8 products align more closely to GAMMA products than other open-source software in terms of radiometric and geometric quality. This understanding of software performance for SAR image processing is key to designing the affordable and scalable solutions needed for the operational application of SAR Sentinel-1 data. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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28 pages, 6985 KiB

Open AccessArticle

Nonsparse SAR Scene Imaging Network Based on Sparse Representation and Approximate Observations

by Hongwei Zhang, Jiacheng Ni, Kaiming Li, Ying Luo and Qun Zhang

Remote Sens. 2023, 15(17), 4126; https://doi.org/10.3390/rs15174126 - 22 Aug 2023

Cited by 4 | Viewed by 1503

Abstract

Sparse-representation-based synthetic aperture radar (SAR) imaging technology has shown superior potential in the reconstruction of nonsparse scenes. However, many existing compressed sensing (CS) methods with sparse representation cannot obtain an optimal sparse basis and only apply to the sensing matrix obtained by exact [...] Read more.

Sparse-representation-based synthetic aperture radar (SAR) imaging technology has shown superior potential in the reconstruction of nonsparse scenes. However, many existing compressed sensing (CS) methods with sparse representation cannot obtain an optimal sparse basis and only apply to the sensing matrix obtained by exact observation, resulting in a low image quality occupying more storage space. To reduce the computational cost and improve the imaging performance of nonsparse scenes, we formulate a deep learning SAR imaging method based on sparse representation and approximated observation deduced from the chirp-scaling algorithm (CSA). First, we incorporate the CSA-derived approximated observation model and a nonlinear transform function within a sparse reconstruction framework. Second, an iterative shrinkage threshold algorithm is adopted to solve this framework, and the solving process is unfolded as a deep SAR imaging network. Third, a dual-path convolutional neural network (CNN) block is designed in the network to achieve the nonlinear transform, dramatically improving the sparse representation capability over conventional transform-domain-based CS methods. Last, we improve the CNN block to develop an enhanced version of the deep SAR imaging network, in which all the parameters are layer-varied and trained by supervised learning. The experiments demonstrate that our proposed two imaging networks outperform conventional CS-driven and deep-learning-based methods in terms of computing efficiency and reconstruction performance of nonsparse scenes. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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18 pages, 9948 KiB

Open AccessArticle

Machine Learning Applied to a Dual-Polarized Sentinel-1 Image for Wind Retrieval of Tropical Cyclones

by Yuyi Hu, Weizeng Shao, Wei Shen, Yuhang Zhou and Xingwei Jiang

Remote Sens. 2023, 15(16), 3948; https://doi.org/10.3390/rs15163948 - 9 Aug 2023

Cited by 7 | Viewed by 1301

Abstract

In this work, three types of machine learning algorithms are applied for synthetic aperture radar (SAR) wind retrieval in tropical cyclones (TCs), and the optimal method is confirmed. In total, 30 Sentinel-1 (S-1) images in dual-polarization (vertical–vertical [VV] and vertical–horizontal [VH] were collected [...] Read more.

In this work, three types of machine learning algorithms are applied for synthetic aperture radar (SAR) wind retrieval in tropical cyclones (TCs), and the optimal method is confirmed. In total, 30 Sentinel-1 (S-1) images in dual-polarization (vertical–vertical [VV] and vertical–horizontal [VH] were collected during the period from 2016 to 2021, which were acquired in interferometric-wide and extra-wide modes with pixels of 10 m and 40 m, respectively. More than 100,000 sub-scenes with a spatial coverage of 3 km are extracted from these images. The dependences of variables estimated from sub-scenes, i.e., VV-polarized and VH-polarized normalized radar cross-section (NRCS), as well as the azimuthal wave cutoff wavelength, on wind speeds from the stepped-frequency microwave radiometer (SFMR) and the soil moisture active passive (SMAP) radiometer are studied, showing the linear relations between wind speed and these three parameters; however, the saturation of VV-polarized NRCS and the azimuthal wave cutoff wavelength is observed. This is the foundation of selecting input variables in machine learning algorithms. Two-thirds of the collocated dataset (20 images) are used for training the process using three machine learning algorithms, i.e., eXtreme Gradient Boosting (XGBoost), Multi-layer Perceptron, and K-Nearest Neighbor, and the coefficients are fitted after training completion through 20 images collocated with SFMR and SMAP data. Another 10 images are taken for validation up to 70 m/s, yielding a 2.53 m/s root mean square error (RMSE) with a 0.96 correlation and 0.12 scatter index (SI) using XGBoost. The result is better than the >5 m/s error achieved using the existing cross-polarized geophysical model function and the other two machine learning algorithms; moreover, the comparison between wind retrievals using XGBoost and Level-2 CyclObs products shows about 4 m/s RMSE and 0.18 SI. This suggests that the machine learning algorithm XGBoost is an effective method for inverting the TC wind field utilizing SAR measurements in dual-polarization. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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19 pages, 6650 KiB

Open AccessTechnical Note

Innovative Rotating SAR Mode for 3D Imaging of Buildings

by Yun Lin, Ying Wang, Yanping Wang, Wenjie Shen and Zechao Bai

Remote Sens. 2024, 16(12), 2251; https://doi.org/10.3390/rs16122251 - 20 Jun 2024

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Three-dimensional SAR imaging of urban buildings is currently a hotspot in the research area of remote sensing. Synthetic Aperture Radar (SAR) offers all-time, all-weather, high-resolution capacity, and is an important tool for the monitoring of building health. Buildings have geometric distortion in conventional [...] Read more.

Three-dimensional SAR imaging of urban buildings is currently a hotspot in the research area of remote sensing. Synthetic Aperture Radar (SAR) offers all-time, all-weather, high-resolution capacity, and is an important tool for the monitoring of building health. Buildings have geometric distortion in conventional 2D SAR images, which brings great difficulties to the interpretation of SAR images. This paper proposes a novel Rotating SAR (RSAR) mode, which acquires 3D information of buildings from two different angles in a single rotation. This new RSAR mode takes the center of a straight track as its rotation center, and obtains images of the same facade of a building from two different angles. By utilizing the differences in geometric distortion of buildings in the image pair, the 3D structure of the building is reconstructed. Compared to the existing tomographic SAR or circular SAR, this method does not require multiple flights in different elevations or observations from varying aspect angles, and greatly simplifies data acquisition. Furthermore, both simulation analysis and actual data experiment have verified the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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22 pages, 9765 KiB

Open AccessTechnical Note

Enhancing SAR Multipath Ghost Image Suppression for Complex Structures through Multi-Aspect Observation

by Yun Lin, Ziwei Tian, Yanping Wang, Yang Li, Wenjie Shen and Zechao Bai

Remote Sens. 2024, 16(4), 637; https://doi.org/10.3390/rs16040637 - 8 Feb 2024

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When Synthetic Aperture Radar (SAR) observes complex structural targets such as oil tanks, it is easily interfered with by multipath signals, resulting in a large number of multipath ghost images in the SAR image, which seriously affect the image clarity. To address this [...] Read more.

When Synthetic Aperture Radar (SAR) observes complex structural targets such as oil tanks, it is easily interfered with by multipath signals, resulting in a large number of multipath ghost images in the SAR image, which seriously affect the image clarity. To address this problem, this paper proposes a multi-aspect multipath suppression method. This method observes complex structural targets from different azimuth angles to obtain a multi-aspect image sequence and then uses the difference in sequence features between the target image and the multipath ghost image with respect to aspect angle to separate them. This paper takes a floating-roof oil tank as an example to analyze the propagation path and the ghost image characteristics of multipath signals under different observation aspects. We conclude that the scattering center of the multipath ghost image changes with the radar observation aspect, whereas the scattering center of the target image does not. This paper uses the Robust Principal Component Analysis (RPCA) method to decompose the image sequence matrix into two parts: a sparse matrix and a low-rank matrix. The low-rank matrix represents the aspect-stable principal component in the image sequence; that is, the real scattering center. The sparse matrix represents the part of the image sequence that deviates from the principal component; that is, the signal that varies with aspect, mainly including multipath signals, sidelobes, anisotropic signals, etc. By reconstructing the low-rank matrix and the sparse matrix, respectively, we can obtain the image after multipath signal suppression and also the multipath ghost image. Both the target and the multipath signal provide useful information. The image after multipath signal suppression is useful for obtaining the structural information of the target, and the multipath ghost image is useful for analyzing the multipath phenomenon of the complex structure target. This paper conducts experimental verification using real airborne SAR data of an external floating roof oil tank and compares three methods: RPCA, PCA, and sub-aperture fusion method. The experiment shows that the RPCA method can better separate the target image and the multipath ghost image. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application)

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