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Radar Imaging Theory, Techniques, and Applications II

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 37392

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Prof. Dr. Kun-Shan Chen

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College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
Interests: wave scattering; radar imaging of terrains and sea; the applications of radar image
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Prof. Dr. Saibun Tjuatja

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Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
Interests: radar imaging; subsurface sensing and imaging; scattering and emission from random media
Special Issues, Collections and Topics in MDPI journals

Dr. Xinwu Li

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Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Interests: environmental radar remote sensing; remote sensing of global change; remote sensing of polar; ecological and urban environments.

Special Issue Information

Dear Colleagues,

The technology of imaging radar, in particular synthetic aperture radar (SAR), has evolved into a powerful and valuable tool for monitoring our Earth. The rapid progress of hardware and software has driven the radar system to offer superb quality images like never before. Remote sensing represents a unique and valuable source of information to monitor changing conditions in the atmosphere and on the Earth’s surface at various spatial and temporal scales. SAR techniques permit all-weather observations and remote sensing. They can provide either needed reconnaissance or quantitative and sustained measurements, even under challenging situations, making SAR a necessary measure to diagnose the Earth’s health. Wave scattering and image formation are naturally connected. The electromagnetic wave carries time, frequency, and Doppler information that can be integrated coherently, through radar tomography, to provide 3D information of the Earth. This Special Issue focuses on reporting new imaging concepts and theory, advanced systems, novel techniques in engineering architecture, and their infusion to the remote sensing of terrain and ocean.

Imaging radar, e.g., SAR, is a complex device that involves electromagnetic waves (i.e., microwaves and RF), antenna, signal and image processing, data handling, and interpretation. The seamless integration of moving platforms and radar sensors is profoundly critical to making SAR function properly. Moreover, SAR applications in remote sensing science and technologies are a broad, interdisciplinary area of research that should be appealing to a broad readership of remote sensing. There are excellent opportunities for earth science researchers, engineers, and application scientists to contribute to this field. This Special Issue will include papers on the various science and engineering aspects of SAR to give the reader a broader picture of radar remote sensing. The Special Issue will consist of state-of-the-art technology reviews covering various topics.

Suggested topics, but not limited to, include:

Fundamentals of radar imaging
New imaging techniques and systems
Radar image filtering, classification, and change detection
InSAR, PolSAR, TopSAR, PolinSAR, TomoSAR techniques and applications
Data and information processing and fusion
Machine/deep learning
Geophysical parameter retrievals
Environmental change monitoring and assessment

Prof. Dr. Kun-Shan Chen
Prof. Dr. Saibun Tjuatja
Dr. Xinwu Li
Guest Editors

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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

Imaging radar
Radar interferometry
Radar tomography
Wave scattering
Image formation
Machine learning
Geophysical parameters retrievals
3D information

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Published Papers (12 papers)

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30 pages, 7029 KiB

Open AccessArticle

SAR Image Simulation Based on Effective View and Ray Tracing

by Ke Wu, Guowang Jin, Xin Xiong, Hongmin Zhang and Limei Wang

Remote Sens. 2022, 14(22), 5754; https://doi.org/10.3390/rs14225754 - 14 Nov 2022

Cited by 7 | Viewed by 4250

Abstract

We present a novel echo-based synthetic aperture radar (SAR) image simulation method that comprehensively utilizes both SAR effective view and ray tracing algorithms. To improve the fidelity of simulated SAR images, we first design an SAR effective view algorithm to process the selected facet target model, with the purpose of discretizing the facets in the SAR effective view into lattice targets and ensuring that the interval of lattice targets is set strictly following the Nyquist sampling law. Then, we combine the ray tracing algorithm and SAR echo time-domain simulation and perform SAR echo time-domain simulation of lattice targets based on ray tracing. Various kinds of backscatter coefficient for each point target can be recorded, corresponding to the number of transmitted pulses within the synthetic aperture time. The echo matrixes of lattice targets are superimposed to generate the raw echo signal. Finally, the raw echo signal is processed by using the range-Doppler (RD) imaging algorithm to obtain the simulated SAR image. We conducted SAR image simulation tests on several facet target models, including car body, assault boat, and airplane, with different material properties. The simulated SAR images obtained by the proposed method were qualitatively and quantitatively evaluated. The experimental results verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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18 pages, 3045 KiB

Open AccessArticle

Real-Time Phaseless Microwave Frequency-Diverse Imaging with Deep Prior Generative Neural Network

by Zhenhua Wu, Fafa Zhao, Man Zhang, Jun Qian and Lixia Yang

Remote Sens. 2022, 14(22), 5665; https://doi.org/10.3390/rs14225665 - 9 Nov 2022

Cited by 3 | Viewed by 1685

Abstract

The millimeter-wave frequency-diverse imaging regime has recently received considerable attention in both the security screening and synthetic aperture radar imaging literature. Considering that the minor systematic errors and alignment errors could still produce heavily corrupted images, these complex-based imaging reconstructions rely heavily on the precise measurement of both phase and amplitude of radiation field patterns and echo signals. In the literature, it is shown that by leveraging phase-retrieval techniques, salient reconstruction images can still be acquired, even in the presence of significant phase errors, which could ease the phase error calibration pressure to a large extent in practical imaging applications. In this paper, in the regime of phaseless frequency-diverse imaging, with the powerful feature inference and generation power of unsupervised generative models, an end-to-end deep prior generative neural network is designed to achieve near real-time imaging. The harsh imaging reconstruction with both the high radiation mode correlations and extremely low scene compression sampling ratio, which are extremely troublesome to tackle for generally applied matched-filter and compressed sensing approach in the current frequency-diverse imaging literature, can still be preferably handled with our reconstruction network. The well-trained reconstruction network is constituted by prior inference and deep generative modules with excellent generative capabilities and significant prior inference abilities. Using simulation experiments with radiation field data, we verify that the integration of phase-free frequency-change imaging with deep learning networks can effectively improve reconstruction capabilities and improve robustness to systematic phase errors. Compared with existing imaging methods, our imaging method has high imaging performance and can even reconstruct targets under low compression ratio conditions, which is somewhat competitive with current state-of-the-art algorithms. Moreover, we find that the proposed method has good anti-noise and stability. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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23 pages, 31402 KiB

Open AccessArticle

Automatic Supraglacial Lake Extraction in Greenland Using Sentinel-1 SAR Images and Attention-Based U-Net

by Di Jiang, Xinwu Li, Ke Zhang, Sebastián Marinsek, Wen Hong and Yirong Wu

Remote Sens. 2022, 14(19), 4998; https://doi.org/10.3390/rs14194998 - 8 Oct 2022

Cited by 8 | Viewed by 2815

Abstract

With global warming, supraglacial lakes play an important role in ice sheet stability and climate change. They are not only the main factors affecting mass balance and sea-level rise but also the key units of surface runoff storage and mass loss. To automatically map the spatiotemporal distribution of supraglacial lakes in Greenland, this paper proposes an attention-based U-Net model with Sentinel-1 SAR imagery. The extraction results show that compared with the traditional network, this method obtains a higher validation coefficient, with an F1 score of 0.971, and it is spatiotemporally transferable, able to realize the extraction of supraglacial lakes in complex areas without ignoring small lakes. In addition, we conducted a case study in the Jakobshavn region and found that the supraglacial lake area peaked in advance between spring and summer due to extreme melting events from 2017 to 2021. Meanwhile, the supraglacial lakes near the 79°N Glacier tended to expand inland during the melting season. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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22 pages, 3557 KiB

Open AccessArticle

SAR Tomography Based on Atomic Norm Minimization in Urban Areas

by Ning Liu, Xinwu Li, Xing Peng and Wen Hong

Remote Sens. 2022, 14(14), 3439; https://doi.org/10.3390/rs14143439 - 17 Jul 2022

Cited by 4 | Viewed by 2245

Abstract

Synthetic aperture radar (SAR) tomography (TomoSAR) is a powerful tool for the three-dimensional (3D) reconstruction of buildings in urban areas. At present, the compressed sensing (CS) technique has been widely used in the TomoSAR inversion of urban areas because of the sparsity of the backscattering power of buildings along the elevation direction. However, this algorithm discretizes the elevation and assumes that the scatterers are located on predetermined finite grids. In fact, scatterers can lie anywhere in the elevation direction, regardless of grid point constraints. The phenomenon of scatterer positioning errors due to elevation discretization is called the off-grid effect, which will affect the height estimation accuracy of TomoSAR. To overcome this problem, we proposed a TomoSAR reconstruction algorithm based on atomic norm minimization (Tomo-ANM) in this paper. Tomo-ANM employs ANM, a continuous compressed sensing technique, to obtain scatterer positions on the continuous dictionary, thus eliminating the off-grid effect. Baseline compensation is necessary to obtain the data of virtual uniform baselines or the samples of uniform data during preprocessing. A fast realization of ANM, IVDST, is utilized to accelerate the process. Tomo-ANM was tested through simulation experiments, and the results confirmed the validity of eliminating the influence of off-grid effects and exhibited an improved location accuracy and detection rate in less time compared with the on-grid TomoSAR algorithm SL1MMER. Real data experiments based on eight staring spotlight TerraSAR-X images showed that Tomo-ANM can improve the accuracy of building height estimation by 4.83% relative to its real height. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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22 pages, 13342 KiB

Open AccessArticle

Time Series Surface Deformation of Changbaishan Volcano Based on Sentinel-1B SAR Data and Its Geological Significance

by Zhiguo Meng, Chuanzeng Shu, Ying Yang, Chengzhi Wu, Xuegang Dong, Dongzhen Wang and Yuanzhi Zhang

Remote Sens. 2022, 14(5), 1213; https://doi.org/10.3390/rs14051213 - 1 Mar 2022

Cited by 11 | Viewed by 3693

Abstract

Monitoring the surface deformation is of great significance, in order to explore the activity and geophysical features of the underground deep pressure source in the volcanic regions. In this study, the time series surface deformation of the Changbaishan volcano is retrieved via Sentinel-1B SAR data, using the SBAS-InSAR method. The main results are as follows. (1) The mean surface deformation velocity in the Changbaishan volcano is uplifted as a whole, while the uplift is locally distributed, which shows a strong correlation with faults. (2) The time series surface deformation of the Changbaishan volcano indicates an apparently seasonal change. (3) The cumulative surface deformation shows a strong correlation with the maximal magnitude and number of annual earthquakes, and it is likely dominated by the maximal magnitude of the annual earthquakes. (4) The single Mogi source model is appropriate to evaluate the deep pressure source in the Changbaishan volcano, constrained by the calculated surface deformation. The optimal estimated depth of the magma chamber is about 6.2 km, and the volume is increased by about 3.2 × 10⁶ m³. According to the time series surface deformation, it is concluded that the tectonic activity and faults, related to the deep pressure source, are pretty active in the Changbaishan volcano. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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17 pages, 3469 KiB

Open AccessArticle

A Novel High-Squint Spotlight SAR Raw Data Simulation Scheme in 2-D Frequency Domain

by Zhengwei Guo, Zewen Fu, Jike Chang, Lin Wu and Ning Li

Remote Sens. 2022, 14(3), 651; https://doi.org/10.3390/rs14030651 - 29 Jan 2022

Cited by 8 | Viewed by 2628

Abstract

Raw data simulation is the front-end work of synthetic aperture radar (SAR), which is of great significance. For high-squint spotlight SAR, the frequency domain simulation algorithm is invalid because of the range-azimuth coupling effect. In order to realize high-squint spotlight SAR raw data simulation in the frequency domain, an algorithm based on coordinate transformation and non-uniform fast Fourier transform (NUFFT) is proposed. This algorithm generates broadside raw data using a two-dimensional (2-D) frequency simulation algorithm; then, coordinate transformation is used by analyzing the characteristics of broadside and high-squint spotlight SAR. After coordinate transformation, NUFFT is carried out to realize the coupling relation in the 2-D frequency domain. Since the coordinate transformation ignores the influence of range walk, the range walk is compensated after NUFFT. As a result, compared with the traditional squint spotlight SAR frequency domain simulation algorithm, the proposed algorithm can improve the accuracy of point and distributed target imaging results, and the efficiency of the proposed algorithm can be significantly improved in contrast the traditional time domain algorithm. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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22 pages, 21548 KiB

Open AccessArticle

Entropy-Based Non-Local Means Filter for Single-Look SAR Speckle Reduction

by Debora Chan, Juliana Gambini and Alejandro C. Frery

Remote Sens. 2022, 14(3), 509; https://doi.org/10.3390/rs14030509 - 21 Jan 2022

Cited by 9 | Viewed by 2517

Abstract

Speckle is an interference phenomenon that contaminates images captured by coherent illumination systems. Due to its multiplicative and non-Gaussian nature, it is challenging to eliminate. The non-local means approach to noise reduction has proven flexible and provided good results. We propose in this [...] Read more.

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model. We obtain the necessary mathematical apparatus (the Fisher information matrix and asymptotic variance of maximum likelihood estimators). The similarity between samples of the patches relies on a parametric statistical test that verifies the evidence whether two samples have the same entropy or not. Then, we build the convolution mask by transforming the p-value into weights with a smooth activation function. The results are encouraging, as the filtered images have a better signal-to-noise ratio, they preserve the mean, and the edges are not severely blurred. The proposed algorithm is compared with three successful filters: SRAD (Speckle Reducing Anisotropic Diffusion), Lee, and FANS (Fast Adaptive Nonlocal SAR Despeckling), showing the new method’s competitiveness. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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21 pages, 5398 KiB

Open AccessArticle

Radar Imaging Statistics of Non-Gaussian Rough Surface: A Physics-Based Simulation Study

by Cheng-Yen Chiang, Kun-Shan Chen, Ying Yang, Yang Zhang and Lingbing Wu

Remote Sens. 2022, 14(2), 311; https://doi.org/10.3390/rs14020311 - 11 Jan 2022

Cited by 6 | Viewed by 2817

Abstract

This paper investigates the radar image statistics of rough surfaces by simulating the scattered signal’s dependence on the surface roughness. Statistically, the roughness characteristics include the height probability density (HPD) and, to the second-order, the power spectral density (PSD). We simulated the radar backscattered signal by computing the far-field scattered field from the rough surface within the antenna beam volume in the context of synthetic aperture radar (SAR) imaging. To account for the non-Gaussian height distribution, we consider microscopic details of the roughness on comparable radar wavelength scales to include specularly, singly, and multiply scatterers. We introduce surface roughness index (RSI) to distinguish the statistical characteristics of rough surfaces with different height distributions. Results suggest that increasing the RMS height does not impact the Gaussian HPD surface but significantly affects the Weibull surface. The results confirm that as the radar frequency increases, or reaches a relatively larger roughness, the surface’s HPD causes significant changes in incoherent scattering due to more frequent multiple scattering contributions. As a result, the speckle move further away from the Rayleigh model. By examining individual RSI, we see that the Gaussian HPD surface is much less sensitive to RMS height than the Weibull HPD surface. We demonstrate that to retrieve the surface parameters (both dielectric and roughness) from the estimated RCS, less accuracy is expected for the non-Gaussian surface than the Gaussian surface under the same conditions. Therefore, results drawn from this study are helpful for system performance evaluations, parameters estimation, and target detection for SAR imaging of a rough surface. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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18 pages, 3211 KiB

Open AccessArticle

An Azimuth Signal-Reconstruction Method Based on Two-Step Projection Technology for Spaceborne Azimuth Multi-Channel High-Resolution and Wide-Swath SAR

by Ning Li, Hanqing Zhang, Jianhui Zhao, Lin Wu and Zhengwei Guo

Remote Sens. 2021, 13(24), 4988; https://doi.org/10.3390/rs13244988 - 8 Dec 2021

Cited by 4 | Viewed by 2969

Abstract

Azimuth non-uniform signal-reconstruction is a critical step for azimuth multi-channel high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) data processing. However, the received non-uniform signal has noise in the actual azimuth multi-channel SAR (MCSAR) operation, which leads to the serious reduction in the signal-to-noise ratio (SNR) of the results processed by a traditional reconstruction algorithm. Aiming to address the problem of reducing the SNR of the traditional reconstruction algorithm in the reconstruction of non-uniform signal with noise, a novel signal-reconstruction algorithm based on two-step projection technology (TSPT) for the MCSAR system is proposed in this paper. The key part of the TSPT algorithm consists of a two-step projection. The first projection is to project the given signal into the selected intermediate subspace, spanned by the integer conversion of the compact support kernel function. This process generates a set of sparse equations, which can be solved efficiently by using the sparse equation solver. The second key projection is to project the first projection result into the subspace of the known sampled signal. The secondary projection can be achieved with a digital linear translation invariant (LSI) filter and generate a uniformly spaced signal. As a result, compared with the traditional azimuth MCSAR signal-reconstruction algorithm, the proposed algorithm can improve SNR and reduce the azimuth ambiguity-signal-ratio (AASR). The processing results of simulated data and real raw data verify the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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21 pages, 7132 KiB

Open AccessArticle

SAR Image Simulation of Complex Target including Multiple Scattering

by Cheng-Yen Chiang, Kun-Shan Chen, Ying Yang, Yang Zhang and Tong Zhang

Remote Sens. 2021, 13(23), 4854; https://doi.org/10.3390/rs13234854 - 29 Nov 2021

Cited by 13 | Viewed by 4686

Abstract

We present a GPU-based computation for simulating the synthetic aperture radar (SAR) image of the complex target. To be more realistic, we included the multiple scattering field and antenna pattern tracking in producing the SAR echo signal for both Stripmap and Spotlight modes. Of the signal chains, the computation of the backscattering field is the most computationally intensive. To resolve the issue, we implement a computation parallelization for SAR echo signal generation. By profiling, the overall processing was identified to find which is the heavy loading stage. To further accommodate the hardware structure, we made extensive modifications in the CUDA kernel function. As a result, the computation efficiency is much improved, with over 224 times the speed up. The computation complexity by comparing the CPU and GPU computations was provided. We validated the proposed simulation algorithm using canonical targets, including a perfectly electric conductor (PEC), dielectric spheres, and rotated/unrotated dihedral corner reflectors. Additionally, the targets can be a multi-layered dielectric coating or a layered medium. The latter case aimed to evaluate the polarimetric response quantitively. Then, we simulated a complex target with various poses relative to the SAR imaging geometry. We show that the simulated images have high fidelity in geometric and radiometric specifications. The decomposition of images from individual scattering bounce offers valuable exploitation of the scattering mechanisms responsible for imaging certain target features. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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14 pages, 1226 KiB

Open AccessTechnical Note

Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images

by Jianjun Huang, Jie Chen, Yanan Guo and Pengbo Wang

Remote Sens. 2022, 14(9), 2141; https://doi.org/10.3390/rs14092141 - 29 Apr 2022

Viewed by 2027

Abstract

Azimuthal multi-angle observation (AMAO) is a novel spaceborne synthetic aperture radar (SAR) technique proposed recently, which significantly improves the acquisition performance of target information. It has highly flexible antenna beams with powerful beam-steering capability while affecting the consistency of radiometric measurements, especially in the high-squint case. This paper mainly focuses on the antenna pattern effect analysis and correction method of AMAO spaceborne SAR. The antenna pattern characteristics of the squint steering beam are first analyzed. On this basis, an analytical model is developed to quantify the antenna pattern effects in AMAO SAR images. Different from the conventional approach that corrects the elevation antenna pattern, an improved antenna pattern correction method, which accounts for the spatial variation in the two-dimensional antenna pattern, is then proposed for AMAO spaceborne SAR. Finally, experiments are conducted to verify the proposed correction method. The results show that the newly proposed method considering the spatial-variant antenna pattern has higher performance than the conventional approach using a reference elevation pattern. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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21 pages, 12222 KiB

Open AccessTechnical Note

Experimental Results of Three-Dimensional Modeling and Mapping with Airborne Ka-Band Fixed-Baseline InSAR in Typical Topographies of China

by Jian Gao, Zhongchang Sun, Huadong Guo, Lideng Wei, Yongjie Li and Qiang Xing

Remote Sens. 2022, 14(6), 1355; https://doi.org/10.3390/rs14061355 - 10 Mar 2022

Cited by 5 | Viewed by 2240

Abstract

Interferometric synthetic aperture radar (InSAR) has become a key technology for producing high-precision digital surface models (DSMs) and digital orthophoto maps (DOMs) in full time and all weathers. Airborne millimeter-wave InSAR, with large-scale and high-resolution imaging, is characterized by high spatial resolution, flexibility, and immunity to loss-of-correlation. This paper introduces our modeling experiments with airborne dual-antenna, Ka-band InSAR regarding typical topographies of China. Ka-band SAR data were acquired in designated experimental areas in flat (Heyang area in Shaanxi) and mountainous areas (Shibing area in Guizhou and Qionglai area in Sichuan). The key processing of the experimental data for DSMs and DOMs is demonstrated in the paper, especially the proposed robust and efficient phase unwrapping (PU) method for the interferometric data and block adjustment method of strip calibration. The results show that the proposed unwrapping method can provide reliable unwrapped phase results in undulating areas, and the block adjustment can carry out consistent calibration for strips with sparse ground control points (GCPs). The accuracy assessment of the DSM shows that the coordinate root mean square error (RSME) of the obtained DSM is less than 2 m in height, and 2.5 m horizontally, which meets the 1:5000 requirement for topographic mapping in difficult areas. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)

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