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Recent Progress and Applications on Multi-Dimensional SAR
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Recent Progress and Applications on Multi-Dimensional SAR

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 35602

Special Issue Editors

Dr. Bingnan Wang

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Guest Editor
National Key Laboratory of Microwave Imaging Technology/Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: signal processing of advanced SAR system; including theory and applications of SAR interferometry; digital array SAR signal processing; synthetic aperture ladar imaging; UAV-SAR imaging and GMTI
Prof. Dr. Liangjiang Zhou

E-Mail
Guest Editor
National Key Laboratory of Microwave Imaging Technology/Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: coherent radar techniques; radar signal processing; radar signal interpretation; digital radar systems; radar imaging

Special Issue Information

Dear Colleagues,

The synthetic aperture radar has the ability to observe the Earth at all times in all weather, and has become an important method of resource exploration, environmental monitoring and assessing disasters. The scattering mechanism of the observed target shows complex characteristics, directly affecting the cognition and understanding of the SAR image. The SAR imaging theory of single observation or simple combination of several observations is not suitable for describing complex scattering characteristics, nor for improving the accuracy of quantitative measurements.

With the development of the SAR imaging technology and promotion of its applicational needs, data acquisition is becoming more and more diverse, gradually developing from single-polarization, single-angle and single-band to multi-polarization, multi-frequency, multi-angle and multi-time. The multi-dimensional SAR provides theoretical and methodological support for the high-precision mapping of complex terrains, three-dimensional ocean exploration, the quantitative monitoring of forest resources and ecological environment elements.

This topic aims to exhibit the observations and applications of multi-dimensional SAR using two or more dimensions, the resolution, frequency, polarization, angle and time, with an emphasis on innovative approaches, including new observation extraction methods, the development of new sensors and the expansion of new applications.

We would like to invite research papers featuring creative imaging mechanisms, advanced sensors, data processing, and feature extraction methods using multi-dimensional SAR. We especially welcome well-prepared, unpublished submissions addressing one or more of the above-mentioned topics.

Dr. Bingnan Wang
Prof. Dr. Liangjiang Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • new concepts on multi-dimensional SAR
  • new sensing technology
  • imaging methods for multi-dimensional SAR
  • multi-angular imaging for scattering characteristic analysis
  • multi-temporal imaging for change detection
  • multi-band imaging and data fusion
  • polarimetric SAR interferometry
  • polarimetric 3D image reconstruction

Published Papers (20 papers)

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21 pages, 5831 KiB  
Article
Layover Detection Using Neural Network Based on Expert Knowledge
by Ye Tian, Chibiao Ding, Minan Shi and Fubo Zhang
Remote Sens. 2022, 14(23), 6087; https://doi.org/10.3390/rs14236087 - 30 Nov 2022
Cited by 1 | Viewed by 1226
Abstract
Layover detection is crucial in 3D array SAR topography reconstruction. However, existing algorithms are not automated and accurate enough in practice. To solve this problem, this paper proposes a novel layover detection method that combines the complex-valued (cv) neural network and expert knowledge [...] Read more.
Layover detection is crucial in 3D array SAR topography reconstruction. However, existing algorithms are not automated and accurate enough in practice. To solve this problem, this paper proposes a novel layover detection method that combines the complex-valued (cv) neural network and expert knowledge to extract features in the amplitude and phase of multi-channel SAR. First, inspired by expert knowledge, a fast Fourier transform (FFT) residual convolutional neural network was developed to eliminate the training divergence of the cv network, deepen networks without extra parameters, and facilitate network learning. Then, another innovative component, phase convolution, was designed to extract phase features of the layover. Subsequently, various cv neural network components were integrated with FFT residual learning blocks and phase convolution on the skeleton of U-Net. Due to the difficulty of obtaining SAR images marked with layover truths, a simulation was performed to gather the required dataset for training. The experimental results indicated that our approach can efficiently determine the layover area with higher precision and fewer noises. The proposed method achieves an accuracy of 97% on the testing dataset, which surpasses previous methods. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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31 pages, 32356 KiB  
Article
Comparison between Different TomoSAR Imaging Models for Airborne Platform Flying at Low Altitude
by Qiancheng Yan, Zekun Jiao, Xiaolan Qiu, Bingnan Wang and Chibiao Ding
Remote Sens. 2022, 14(21), 5452; https://doi.org/10.3390/rs14215452 - 29 Oct 2022
Cited by 2 | Viewed by 1378
Abstract
The classical planar-wavefront-based TomoSAR imaging model suffers from the problem that the effective integration interval is not enough to cover the target distribution region in the low-altitude airborne case. It will lead to a deterioration of the performance of tomogram reconstruction and inaccuracy [...] Read more.
The classical planar-wavefront-based TomoSAR imaging model suffers from the problem that the effective integration interval is not enough to cover the target distribution region in the low-altitude airborne case. It will lead to a deterioration of the performance of tomogram reconstruction and inaccuracy of estimated scatterers. This paper reviews the exact and approximate forms of the aforementioned inaccurate model based on planar wavefront and points out the problem with the conventional model. To solve this problem, we propose spherical wavefront models with the exact form or an approximate form of the slant range formula. The estimated variable for the scatterer’s location is converted from elevation to off-nadir angle, and the effective integration interval has been extended. In addition, we explore relationships between the exact form of the conventional model and the exact form of the proposed model, and the relationship between the approximate form of the conventional model and the approximate form of the proposed model. This provides a basis for modifying the inversion algorithm that is designed based on the conventional model to adapt to the low-altitude airborne case. Eventually, through experiments based on simulated data and measured data, the imprecise reconstructions obtained with the conventional model are demonstrated, and the correctness of spherical wavefront models and the effectiveness of transformation between models are proved. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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18 pages, 7460 KiB  
Article
Complex-Valued Sparse SAR-Image-Based Target Detection and Classification
by Chen Song, Jiarui Deng, Zehao Liu, Bingnan Wang, Yirong Wu and Hui Bi
Remote Sens. 2022, 14(17), 4366; https://doi.org/10.3390/rs14174366 - 02 Sep 2022
Cited by 2 | Viewed by 1368
Abstract
It is known that synthetic aperture radar (SAR) images obtained by typical matched filtering (MF)-based algorithms always suffer from serious noise, sidelobes and clutter. However, the improvement in image quality means that the complexity of SAR systems will increase, which affects the applications [...] Read more.
It is known that synthetic aperture radar (SAR) images obtained by typical matched filtering (MF)-based algorithms always suffer from serious noise, sidelobes and clutter. However, the improvement in image quality means that the complexity of SAR systems will increase, which affects the applications of SAR images. The introduction of sparse signal processing technologies into SAR imaging proposes a new way to solve this problem. Sparse SAR images obtained by sparse recovery algorithms show better image performance than typical complex SAR images with lower sidelobes and higher signal-to-noise ratios (SNR). As the most widely applied fields of SAR images, target detection and target classification rely on SAR images with high quality. Therefore, in this paper, a target detection framework based on sparse images recovered by complex approximate message passing (CAMP) algorithm and a novel classification network via sparse images reconstructed by the new iterative soft thresholding (BiIST) algorithm are proposed. Experimental results show that sparse SAR images have better performance whether for target classification or for target detection than the images recovered by MF-based algorithms, which validates the huge application potentials of sparse images. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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20 pages, 6524 KiB  
Article
Elevation Multi-Channel Imbalance Calibration Method of Digital Beamforming Synthetic Aperture Radar
by Hao Chen, Feng Ming, Liang Li and Guikun Liu
Remote Sens. 2022, 14(17), 4350; https://doi.org/10.3390/rs14174350 - 01 Sep 2022
Viewed by 1269
Abstract
The digital beamforming synthetic aperture radar (DBF-SAR) is proposed by scholars as a promising solution to overcome the constraint of the minimum antenna area of the traditional single-channel SAR to achieve high resolution and wide swath (HRWS) by scan-on-receive (SCORE) in the elevation [...] Read more.
The digital beamforming synthetic aperture radar (DBF-SAR) is proposed by scholars as a promising solution to overcome the constraint of the minimum antenna area of the traditional single-channel SAR to achieve high resolution and wide swath (HRWS) by scan-on-receive (SCORE) in the elevation multiple channel. However, the inevitable channel imbalance between the elevation channels of DBF-SAR will degrade the DBF-SAR image quality. In this paper, we present a method to estimate the sampling time delay error, amplitude error and phase error based on the external calibration data. For the sampling time delay deviation, we adopt to calculate the statistical average of the position deviation of several external calibration points in the reference channel image with that of the error channel image. To avoid noise interference, we image the DBF-SAR original echo-carrying amplitude information to obtain the amplitude error between channels by dividing the absolute values of the complex image data of the error channel. Due to the phase error between channels, the image contrast will decrease. Therefore, the problem of estimating the phase error can be transformed into the problem of maximizing the image contrast. So, in this paper, we use the gradient descent method to optimally estimate the phase error. Finally, the effectiveness of the method is verified by the simulation of airborne measured data and simulation data. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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19 pages, 20909 KiB  
Article
A Polarization Stacking Method for Optimizing Time-Series Interferometric Phases of Distributed Scatterers
by Peng Shen, Changcheng Wang and Jun Hu
Remote Sens. 2022, 14(17), 4168; https://doi.org/10.3390/rs14174168 - 25 Aug 2022
Cited by 3 | Viewed by 1285
Abstract
For time-series interferometric phases optimization of distributed scatterers (DSs), the SqueeSAR technology used the phase linking (PL) to extract the equivalent single-master (ESM) interferometric phases from the multilooking time-series coherence matrix. The Cramer–Rao lower bound (CRLB) for the PL describes the highest achievable [...] Read more.
For time-series interferometric phases optimization of distributed scatterers (DSs), the SqueeSAR technology used the phase linking (PL) to extract the equivalent single-master (ESM) interferometric phases from the multilooking time-series coherence matrix. The Cramer–Rao lower bound (CRLB) for the PL describes the highest achievable estimation accuracy of the ESM phases, which depends on the number of looks and the time-series coherence magnitude matrix. With the abundance of time-series polarimetric SAR data, many scholars have studied the coherence magnitude-based polarimetric optimization methods for optimizing the DS’s time-series interferometric phases, for example, the widely-used exhaustive search polarimetric optimization (ESPO) algorithm. However, the traditional polarimetric optimization methods select the boundary extremums of the coherence region (CR) as the optimized complex coherence, which is usually biased from the free-noise one. Currently, in the polarimetric InSAR (PolInSAR) technology, Shen et al. innovatively considered polarimetric information as statistical samples and proposed the total power (TP) coherency matrix construction method for increasing the number of looks and reducing the interferometric phase noise. Therefore, to optimize the time-series interferometric phases for DS, this paper proposes performing a polarization stacking and extending the PolInSAR TP construction to the time-series PolInSAR (TSPolInSAR) data configuration, called the time-series TP (TSTP) method. Simulated and real experiments prove that the new TSTP construction method has better performance and higher efficiency than the single polarimetric and the traditional ESPO algorithms. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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23 pages, 6614 KiB  
Article
Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
by Songtao Han, Haijun Man, Mei Wang, Zhijin Zhou, Jianfeng Cao, Wei Gao, Lue Chen and Jinsong Ping
Remote Sens. 2022, 14(16), 4117; https://doi.org/10.3390/rs14164117 - 22 Aug 2022
Viewed by 1338
Abstract
Delta-Differential One-Way Ranging (DeltaDOR) is widely used in deep spacecraft navigation, and cross support could enhance navigation accuracy with more interferometry baselines and longer baseline. In China Mars mission Tianwen-1, formal joint cross-support interferometry tracking between China Satellite Launch and TT&C General (CLTC) [...] Read more.
Delta-Differential One-Way Ranging (DeltaDOR) is widely used in deep spacecraft navigation, and cross support could enhance navigation accuracy with more interferometry baselines and longer baseline. In China Mars mission Tianwen-1, formal joint cross-support interferometry tracking between China Satellite Launch and TT&C General (CLTC) and European Space Operations Center (ESOC) under commercial contract was conducted around the critical stages of the mission, such as Mars orbit insertion. Cross-support interferometry is a new challenge to CLTC, as the correlator for routine DeltaDOR measurements do not fit for cross support, because of observable definition, blind station clock searching, and so on. This paper discusses the new method and algorithm adopted in joint cross support, especially for spacecraft tone signal processing and clock estimation when correlating with the data of two stations from different agencies. Results of the cross-support interferometry tracking activities are also analyzed. Observables from CLTC and ESOC are consistent with each other, and the difference in observables is in the order of tens of ps. All the baselines are induced to evaluate the accuracy of the spacecraft orbit determined and predicted by CLTC, and the DeltaDOR residuals have a root-mean-square (RMS) better than 0.5 ns (the goal is 1 ns), which could enhance the confidence of the orbit accuracy and the effectiveness of control parameters during critical orbit operation. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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18 pages, 28955 KiB  
Article
A Hybrid Model Based on Superpixel Entropy Discrimination for PolSAR Image Classification
by Jili Sun, Lingdong Geng and Yize Wang
Remote Sens. 2022, 14(16), 4116; https://doi.org/10.3390/rs14164116 - 22 Aug 2022
Cited by 3 | Viewed by 1352
Abstract
Superpixel segmentation is widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, the classification method using simple majority voting cannot easily handle evidence conflicts in a single superpixel. At present, there is no method to evaluate the quality of superpixel classification. [...] Read more.
Superpixel segmentation is widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, the classification method using simple majority voting cannot easily handle evidence conflicts in a single superpixel. At present, there is no method to evaluate the quality of superpixel classification. To solve the above problems, this paper proposes a hybrid classification model based on superpixel entropy discrimination (SED), and constructs a two-level cascade classifier. Firstly, a light gradient boosting machine (LGBM) was used to process large-dimensional input features, and simple linear iterative clustering (SLIC) was integrated to obtain the primary classification results based on superpixels. Secondly, information entropy was introduced to evaluate the quality of superpixel classification, and a complex-valued convolutional neural network (CV-CNN) was used to reclassify the high-entropy superpixels to obtain the secondary classification results. Experiments with two measured PolSAR datasets show that the overall accuracy of both classification methods exceeded 97%. This method suppressed the evidence conflict in a single superpixel and the inaccuracy of superpixel segmentation. The test time of our proposed method was shorter than that of CV-CNN, and using only 55% of CV-CNN test data could achieve the same accuracy as using CV-CNN for the whole image. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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21 pages, 24326 KiB  
Article
Elevation Extraction from Spaceborne SAR Tomography Using Multi-Baseline COSMO-SkyMed SAR Data
by Lang Feng, Jan-Peter Muller, Chaoqun Yu, Chao Deng and Jingfa Zhang
Remote Sens. 2022, 14(16), 4093; https://doi.org/10.3390/rs14164093 - 21 Aug 2022
Cited by 2 | Viewed by 1657
Abstract
SAR tomography (TomoSAR) extends SAR interferometry (InSAR) to image a complex 3D scene with multiple scatterers within the same SAR cell. The phase calibration method and the super-resolution reconstruction method play a crucial role in 3D TomoSAR imaging from multi-baseline SAR stacks, and [...] Read more.
SAR tomography (TomoSAR) extends SAR interferometry (InSAR) to image a complex 3D scene with multiple scatterers within the same SAR cell. The phase calibration method and the super-resolution reconstruction method play a crucial role in 3D TomoSAR imaging from multi-baseline SAR stacks, and they both influence the accuracy of the 3D SAR tomographic imaging results. This paper presents a systematic processing method for 3D SAR tomography imaging. Moreover, with the newly released TanDEM-X 12 m DEM, this study proposes a new phase calibration method based on SAR InSAR and DEM error estimation with the super-resolution reconstruction compressive sensing (CS) method for 3D TomoSAR imaging using COSMO-SkyMed Spaceborne SAR data. The test, fieldwork, and results validation were executed at Zipingpu Dam, Dujiangyan, Sichuan, China. After processing, the 1 m resolution TomoSAR elevation extraction results were obtained. Against the terrestrial Lidar ‘truth’ data, the elevation results were shown to have an accuracy of 0.25 ± 1.04 m and a RMSE of 1.07 m in the dam area. The results and their subsequent validation demonstrate that the X band data using the CS method are not suitable for forest structure reconstruction, but are fit for purpose for the elevation extraction of manufactured facilities including buildings in the urban area. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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19 pages, 5287 KiB  
Article
A Novel Phase Compensation Method for Urban 3D Reconstruction Using SAR Tomography
by Hongliang Lu, Jili Sun, Jili Wang and Chunle Wang
Remote Sens. 2022, 14(16), 4071; https://doi.org/10.3390/rs14164071 - 20 Aug 2022
Cited by 2 | Viewed by 1335
Abstract
Synthetic aperture radar (SAR) tomography (TomoSAR) has been widely used in the three-dimensional (3D) reconstruction of urban areas using the multi-baseline (MB) SAR data. For urban scenarios, the MB SAR data are often acquired by repeat-pass using the spaceborne SAR system. Such a [...] Read more.
Synthetic aperture radar (SAR) tomography (TomoSAR) has been widely used in the three-dimensional (3D) reconstruction of urban areas using the multi-baseline (MB) SAR data. For urban scenarios, the MB SAR data are often acquired by repeat-pass using the spaceborne SAR system. Such a data stack generally has long time baselines, which result in different atmospheric disturbances of the data acquired by different tracks. These factors can lead to the presence of phase errors (PEs). PEs are multiplicative noise for observation data, which can cause diffusion and defocus in TomoSAR imaging and seriously affect the extraction of target 3D information. In this paper, we combine the methods of the block-building network (BBN) and phase gradient autofocus (PGA) to propose a novel phase compensation method called BBN-PGA. The BBN-PGA method can effectively and efficiently compensate for PEs of the MB SAR data over a wide area and improve the accuracy of 3D reconstruction of urban areas. The applicability of this proposed BBN-PGA method is proved by using simulated data and the spaceborne MB SAR data acquired by the TerraSAR-X satellite over an area in Barcelona, Spain. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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18 pages, 7665 KiB  
Article
Multi-Band and Polarization SAR Images Colorization Fusion
by Xinchen Li, Dan Jing, Yachao Li, Liang Guo, Liang Han, Qing Xu, Mengdao Xing and Yihua Hu
Remote Sens. 2022, 14(16), 4022; https://doi.org/10.3390/rs14164022 - 18 Aug 2022
Cited by 1 | Viewed by 1752
Abstract
The image fusion of multi-band and multi-polarization synthetic aperture radar (SAR) images can improve the efficiency of band and polarization information processing. In this paper, we introduce a fusion method that simultaneously fuses multi-band and polarization SAR images. In the method, we first [...] Read more.
The image fusion of multi-band and multi-polarization synthetic aperture radar (SAR) images can improve the efficiency of band and polarization information processing. In this paper, we introduce a fusion method that simultaneously fuses multi-band and polarization SAR images. In the method, we first use non-subsampled shearlet transform (NSST) to fuse multi-band and polarization SAR images. The sub-band images decomposed from the NSST are fused by the coefficient of variation (CV) and phase consistency (PC) weighted fusion rules. Subsequently, we extract the band and polarization difference information from the multi-band and polarization SAR images. The fusion image is finally colorized according to the band and polarization differences. In the experiments, we used Ka and S-band multi-polarization SAR images to test the fusion performance. The experiment results prove that the proposed fused images not only preserve much valuable information but also can be interpreted easily. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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20 pages, 2303 KiB  
Article
A Controllable Suppression Jamming Method against SAR Based on Active Radar Transponder
by Guikun Liu, Liang Li, Feng Ming, Xilong Sun and Jun Hong
Remote Sens. 2022, 14(16), 3949; https://doi.org/10.3390/rs14163949 - 14 Aug 2022
Cited by 6 | Viewed by 1419
Abstract
A chirp-mismatch echo signal can be generated by exchanging the I and Q baseband signals of a received SAR signal. In this paper, the basic generation principles of a chirp-mismatch echo signal are analyzed. Then, a suppression jamming method with controllable jamming position [...] Read more.
A chirp-mismatch echo signal can be generated by exchanging the I and Q baseband signals of a received SAR signal. In this paper, the basic generation principles of a chirp-mismatch echo signal are analyzed. Then, a suppression jamming method with controllable jamming position and coverage area is proposed. This method firstly performs chirp-mismatch processing on the received SAR signals, then controls the range jamming coverage and center position through range shift-frequency modulation and time delay, and controls the azimuth jamming coverage and center position through motion modulation and azimuth shift-frequency modulation. Theoretical analysis and simulation results show that this method can effectively control the location and coverage of a jamming result without convolution modulation, and it is easy to implement in engineering. The simulation results verify the correctness of the theoretical model, which can provide a basis for the implementation and application of SAR jamming based on the active radar transponder. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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20 pages, 5657 KiB  
Article
A Novel Polarimetric Channel Imbalance Phase Estimation Method Based on the Rotated Double-Bounce Backscatters in Urban Areas
by Songtao Shangguan, Xiaolan Qiu, Bin Han, Wenju Liu and Kun Fu
Remote Sens. 2022, 14(13), 3177; https://doi.org/10.3390/rs14133177 - 01 Jul 2022
Cited by 3 | Viewed by 1279
Abstract
Polarization calibration without artificial calibrators has been one of the focuses of research and discussion for PolSAR communities. However, there is limited research on the treatment of dual-polarization systems and the calibration methods for getting rid of distributed targets. In this paper, we [...] Read more.
Polarization calibration without artificial calibrators has been one of the focuses of research and discussion for PolSAR communities. However, there is limited research on the treatment of dual-polarization systems and the calibration methods for getting rid of distributed targets. In this paper, we contribute to proposing a new and convenient method for estimating the polarimetric channel imbalance phase at the transmitter and receiver, which can be used for both quad-pol and dual-pol SAR systems. We found a brand-new reference object in the urban area scene, namely the effective dihedrals. A statistical calculation method was proposed correspondingly, which obtained an effective estimation of the channel imbalance phases. The theoretical explanation of the proposed method was consistent with the statistical phenomena presented in the experiments. The technique was illustrated and verified through C-band SAR images, including GaoFen-3 (GF-3) data and Sentinel-1 data. The technique was also validated and successfully applied in airborne SAR data of P, L, S, C, and X bands. The estimation error could be within 7° when crosstalk items were less than −30 dB. The method realizes a fast and low-cost dual-polarization phase imbalance estimation and provides a new technical approach to supplement the traditional tropical-rainforest-based quad-pol system calibration. The method can be conveniently applied to the monitoring of polarization distortion parameters, ensuring good polarization SAR data quality. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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25 pages, 18204 KiB  
Article
Forest Height Estimation Approach Combining P-Band and X-Band Interferometric SAR Data
by Kunpeng Xu, Lei Zhao, Erxue Chen, Kun Li, Dacheng Liu, Tao Li, Zengyuan Li and Yaxiong Fan
Remote Sens. 2022, 14(13), 3070; https://doi.org/10.3390/rs14133070 - 26 Jun 2022
Cited by 2 | Viewed by 2206
Abstract
Forest height is an essential parameter used to derive important information about forest ecosystems, such as forest above-ground biomass. In this article, a forest height estimation approach combining P-band and X-band interferometric synthetic aperture radar (InSAR) was introduced. The forest height was estimated [...] Read more.
Forest height is an essential parameter used to derive important information about forest ecosystems, such as forest above-ground biomass. In this article, a forest height estimation approach combining P-band and X-band interferometric synthetic aperture radar (InSAR) was introduced. The forest height was estimated using the difference in the penetration of long- and short-wavelength radars to the forest. That is, the P-band and X-band InSAR data were used to extract the digital terrain model (DTM) and digital surface model (DSM), respectively. For the DTM, an improved time-frequency (TF) analysis method was used to reduce the effect of forest scatterers on the extraction of a pure understory terrain phase based on P-band InSAR. For the DSM, a novel compensation algorithm based on a multi-layer model (MLM) was proposed to remove the penetration bias of the X-band. Compared to the existing method based on the infinitely deep uniform volumes (IDUV) model, the MLM-based method is more in line with the characteristics of forest structure and the scattering mechanism for X-band InSAR. The airborne P-band repeat-pass InSAR and spaceborne X-band (TanDEM-X) single-pass InSAR data were used to verify the proposed method over the study area in the Saihanba Forest Farm in Hebei, China. The results demonstrated that the improved TF method can achieve high-precision DTM extraction based on P-band InSAR data, and the root mean square error (RMSE) was 0.94 m. The proposed MLM-based compensation method of the DSM achieved a smaller error (RMSE: 1.67 m) compared to the IDUV-based method (RMSE: 3.01 m). Under the same DTM extracted by P-band InSAR, the estimation accuracy of forest height based on the MLM method was 86.58% (RMSE: 1.81 m), which was 8.49% higher than that of the IDUV-based method (RMSE: 2.98 m). Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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20 pages, 15527 KiB  
Article
Elevation Resolution Enhancement Method Using Non-Ideal Linear Motion Error of Airborne Array TomoSAR
by Ling Yang, Fubo Zhang, Zhuo Zhang, Longyong Chen, Dawei Wang, Yaqian Yang and Zhenhua Li
Remote Sens. 2022, 14(12), 2891; https://doi.org/10.3390/rs14122891 - 16 Jun 2022
Cited by 1 | Viewed by 1498
Abstract
Airborne array tomographic synthetic aperture radar (TomoSAR) is a major breakthrough, which can obtain three-dimensional (3D) information of layover scenes in a single pass. As a high-resolution SAR, airborne array TomoSAR has considerable potential for 3D applications. However, the original TomoSAR elevation resolution [...] Read more.
Airborne array tomographic synthetic aperture radar (TomoSAR) is a major breakthrough, which can obtain three-dimensional (3D) information of layover scenes in a single pass. As a high-resolution SAR, airborne array TomoSAR has considerable potential for 3D applications. However, the original TomoSAR elevation resolution is limited by the baseline and platform length. In this study, a novel method for enhancing the elevation resolution is proposed. First, the actual curve trajectory observation model of airborne array TomoSAR is established. Subsequently, multi-channel image data are substituted into the model to obtain the observation equation. Furthermore, the azimuth and elevation directions of the two-dimensional observation scene are modeled uniformly. The scene reconstruction is realized through the two-dimensional joint solution. Finally, the observation equation is sparsely solved according to the sparse distribution characteristics of the target to obtain the image. The performance of the proposed method is verified via simulation and real-data experiments. The experimental results indicate that, compared with the traditional elevation resolution enhancement method, the proposed method improves the elevation resolution by two times. The proposed method also provides a new thinking for high-resolution SAR 3D imaging. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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16 pages, 7428 KiB  
Article
SAR Image Fusion Classification Based on the Decision-Level Combination of Multi-Band Information
by Jinbiao Zhu, Jie Pan, Wen Jiang, Xijuan Yue and Pengyu Yin
Remote Sens. 2022, 14(9), 2243; https://doi.org/10.3390/rs14092243 - 07 May 2022
Cited by 2 | Viewed by 1648
Abstract
Synthetic aperture radar (SAR) is an active coherent microwave remote sensing system. SAR systems working in different bands have different imaging results for the same area, resulting in different advantages and limitations for SAR image classification. Therefore, to synthesize the classification information of [...] Read more.
Synthetic aperture radar (SAR) is an active coherent microwave remote sensing system. SAR systems working in different bands have different imaging results for the same area, resulting in different advantages and limitations for SAR image classification. Therefore, to synthesize the classification information of SAR images into different bands, an SAR image fusion classification method based on the decision-level combination of multi-band information is proposed in this paper. Within the proposed method, the idea of Dempster–Shafer evidence theory is introduced to model the uncertainty of the classification result of each pixel and used to combine the classification results of multiple band SAR images. The convolutional neural network is used to classify single-band SAR images. Calculate the belief entropy of each pixel to measure the uncertainty of single-band classification, and generate the basic probability assignment function. The idea of the term frequency-inverse document frequency in natural language processing is combined with the conflict coefficient to obtain the weight of different bands. Meanwhile, the neighborhood classification of each pixel in different band sensors is considered to obtain the total weight of each band sensor, generate weighted average BPA, and obtain the final ground object classification result after fusion. The validity of the proposed method is verified in two groups of multi-band SAR image classification experiments, and the proposed method has effectively improved the accuracy compared to the modified average approach. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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24 pages, 19269 KiB  
Article
Multi-Rotor UAV-Borne PolInSAR Data Processing and Preliminary Analysis of Height Inversion in Urban Area
by Zexin Lv, Xiaolan Qiu, Yao Cheng, Songtao Shangguan, Fangfang Li and Chibiao Ding
Remote Sens. 2022, 14(9), 2161; https://doi.org/10.3390/rs14092161 - 30 Apr 2022
Cited by 5 | Viewed by 1728
Abstract
Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) has many useful applications, especially in forest areas. With the development of SAR miniaturization technology, researchers can install PolInSAR on small unmanned aerial vehicles (UAV), which can reduce flight costs. Limited by size and power, UAV-borne SAR [...] Read more.
Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) has many useful applications, especially in forest areas. With the development of SAR miniaturization technology, researchers can install PolInSAR on small unmanned aerial vehicles (UAV), which can reduce flight costs. Limited by size and power, UAV-borne SAR usually works in a high-frequency band, which restricts its application to such things as vegetation height inversion. While on the other hand, the high resolution acquired under a short wavelength promises its application in urban areas. However, there are fewer studies on the application of PolInSAR in urban areas compared with that in forest areas. In this paper, we propose a processing method for a Ku-band multi-rotor-UAV-borne PolInSAR and provide a preliminary analysis of height inversion results on its data from the Fudan campus in Shanghai. We obtain the digital surface model (DSM) of different polarization modes and the DSM of polarimetric interferometry optimal decomposition in this area, whose RMSE is 2.88 m. On this basis, the elevation inversion results of targets such as buildings, lampposts, and trees are compared and analyzed. We preliminarily explore and analyze the reasons for the different results of different targets. To this end, we propose a mathematical derivation of the relationship between the interferometric phase between PolInSAR and InSAR of Pauli decomposition. We also perform a simulation to analyze the relationship between the phase center height of Pauli decomposition and PolInSAR under different cases. It provides a reference for the application of small UAV-borne PolInSAR in urban areas. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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22 pages, 2695 KiB  
Article
Fourfold Bounce Scattering-Based Reconstruction of Building Backs Using Airborne Array TomoSAR Point Clouds
by Xiaowan Li, Fubo Zhang, Xingdong Liang, Yanlei Li, Qichang Guo, Yangliang Wan, Xiangxi Bu and Yunlong Liu
Remote Sens. 2022, 14(8), 1937; https://doi.org/10.3390/rs14081937 - 17 Apr 2022
Cited by 5 | Viewed by 1676
Abstract
Building reconstruction using high-resolution tomographic synthetic aperture radar (TomoSAR) point clouds has been very attractive in numerous applications, such as urban planning and dynamic city modeling. However, for side-looking TomoSAR, it is a challenge to reconstruct the obscured backs of buildings using traditional [...] Read more.
Building reconstruction using high-resolution tomographic synthetic aperture radar (TomoSAR) point clouds has been very attractive in numerous applications, such as urban planning and dynamic city modeling. However, for side-looking TomoSAR, it is a challenge to reconstruct the obscured backs of buildings using traditional single-bounce scattering-based methods. It comes to our attention that the higher-order scattering points in airborne array TomoSAR point clouds may provide rich information on the backs of buildings. In this paper, the fourfold bounce (FB) scattering model of combined buildings in airborne array TomoSAR is derived, which not only explains the cause of FB scattering but also gives the distribution pattern of FB scattering points. Furthermore, a novel FB scattering-based method for the reconstruction of building backs is proposed. First, a two-step geometric constraint is used to detect the candidate FB scattering points. Subsequently, the FB scattering points are further detected by seed point selection and density estimation in the radar coordinate system. Finally, the backs of buildings can be reconstructed using the footprint inverted from the FB scattering points and the height information of the illuminated facades. To verify the FB scattering model and the effectiveness of the proposed method, the results from the simulated point clouds and the real airborne array TomoSAR point clouds are presented. Compared with the traditional roof point-based methods, the outstanding advantage of the proposed method is that it allows for the high-precision reconstruction of building backs, even in the case of poor roof points. Moreover, this paper may provide a novel perspective for the three-dimensional (3D) reconstruction of dense urban areas. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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21 pages, 8651 KiB  
Article
InSAR Study of Landslides: Early Detection, Three-Dimensional, and Long-Term Surface Displacement Estimation—A Case of Xiaojiang River Basin, China
by Hongying Jia, Yingjie Wang, Daqing Ge, Yunkai Deng and Robert Wang
Remote Sens. 2022, 14(7), 1759; https://doi.org/10.3390/rs14071759 - 06 Apr 2022
Cited by 11 | Viewed by 3408
Abstract
Landslides, a major natural geohazard, obstruct municipal constructions and may destroy villages and towns, at worst causing significant casualties and economic losses. Interferometric Synthetic Aperture Radar (InSAR) technique offers distinct advantages on landslide detection and monitoring. In this paper, a more systematic workflow [...] Read more.
Landslides, a major natural geohazard, obstruct municipal constructions and may destroy villages and towns, at worst causing significant casualties and economic losses. Interferometric Synthetic Aperture Radar (InSAR) technique offers distinct advantages on landslide detection and monitoring. In this paper, a more systematic workflow is designed for InSAR study of landslides, in terms of three levels: (i) early detection on regional scale, (ii) three-dimensional (3D) surface displacement rates estimation on detailed scale, and (iii) time series analysis on long-term temporal scale. The proposed workflow is applied for landslide research on the Xiaojiang River Basin, China, using ascending and descending Sentinel-1 images acquired from March 2017 to May 2019. First, the landslide inventory has been mapped and updated using InSAR stacking method, supporting geohazard prevention on a regional scale. A total of 22 active landslides are identified, ranging from medium to super large scale. Compared with the existing inventory, three unrecorded landslides are newly detected by our approach, and five recorded landslides are detected significant expansion of their boundaries. Then, specific to a detected landslide, Baobao landslide, a Total Least Squares–Kalman Filter-based approach is presented. Two outcomes are provided for further spatial-temporal pattern analysis: 3D displacement rates, providing an intuitive insight on the spatial characteristics and sliding direction of landslide, which are analyzed to deep the understanding of its kinematic mechanism, and long-term time series, which contribute to deduce the dynamic evolution of landslide, presenting benefits in landslide forecasting. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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28 pages, 21142 KiB  
Article
A Comparative Study on Classification Features between High-Resolution and Polarimetric SAR Images through Unsupervised Classification Methods
by Junrong Qu, Xiaolan Qiu, Wei Wang, Zezhong Wang, Bin Lei and Chibiao Ding
Remote Sens. 2022, 14(6), 1412; https://doi.org/10.3390/rs14061412 - 15 Mar 2022
Cited by 3 | Viewed by 2663
Abstract
Feature extraction and comparison of synthetic aperture radar (SAR) data of different modes such as high resolution and full polarization have important guiding significance for SAR image applications. In terms of image and physical domain for higher spatial resolution single-polarized and coarser spatial [...] Read more.
Feature extraction and comparison of synthetic aperture radar (SAR) data of different modes such as high resolution and full polarization have important guiding significance for SAR image applications. In terms of image and physical domain for higher spatial resolution single-polarized and coarser spatial resolution quad-pol SAR data, this paper analyzes and compares the feature extraction with unsupervised classification methods. We discover the correlation and complementarity between high-resolution image feature and quad-pol physical scattering information. Therefore, we propose an information fusion strategy, that can conduct unsupervised learning of the landcover classes of SAR images obtained from multiple imaging modes. The medium-resolution polarimetric SAR (PolSAR) data and the high-resolution single-polarized data of the Gaofen-3 satellite are adopted for the selected experiments. First, we conduct the Freeman–Durden decomposition and H/alpha-Wishart classification method on PolSAR data for feature extraction and classification, and use the Deep Convolutional Embedding Clustering (DCEC) algorithm on single-polarized data for unsupervised classification. Then, combined with the quantitative evaluation by confusion matrix and mutual information, we analyze the correlation between characteristics of image domain and physics domain and discuss their respective advantages. Finally, based on the analysis, we propose a refined unsupervised classification method combining image information of high-resolution data and physics information of PolSAR data, that optimizes the classification results of both the urban buildings and the vegetation areas. The main contribution of this comparative study is that it promotes the understanding of the landcover classification ability of different SAR imaging modes and also provides some guidance for future work to combine their respective advantages for better image interpretation. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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13 pages, 6844 KiB  
Technical Note
Ground Moving Target Detection and Estimation for Airborne Multichannel Radar Based on Coherent Difference Processing
by Chong Song, Bingnan Wang, Maosheng Xiang, Weidi Xu, Zhongbin Wang, Yachao Wang and Xiaofan Sun
Remote Sens. 2022, 14(14), 3325; https://doi.org/10.3390/rs14143325 - 10 Jul 2022
Cited by 1 | Viewed by 1357
Abstract
Ground moving targets with slow velocity and low radar cross-section (RCS) are usually embedded in the clutter Doppler spectrum. To achieve the detection and estimation of such targets, a novel method operating in the range-Doppler domain is developed for airborne multichannel radar systems. [...] Read more.
Ground moving targets with slow velocity and low radar cross-section (RCS) are usually embedded in the clutter Doppler spectrum. To achieve the detection and estimation of such targets, a novel method operating in the range-Doppler domain is developed for airborne multichannel radar systems. The interferometric phases that are sensitive to moving targets are obtained by coherent difference processing (CDP) for target detection. Moreover, the amplitude is utilized as complementary information to improve the detection performance. Then, a matched filter bank is designed and applied to the CDP processed data to complete the parameter estimation. The proposed method provides the benefits of high efficiency and robustness, since it does not involve matrix inversion, and it does not require homogeneous clutter assumption unlike adaptive algorithms. Experiments on real data acquired by an airborne X-band four-channel radar system demonstrate its effectiveness. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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