Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
2nd Edition Radar and Sonar Imaging and Processing
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

2nd Edition Radar and Sonar Imaging and Processing

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 (15 August 2021) | Viewed by 45511

Special Issue Editors

Prof. Dr. Andrzej Stateczny

E-Mail Website
Guest Editor
Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk Technical University, Narutowicza St. 11/12, Gdansk, Poland
Interests: radar navigation; comparative (terrain-based) navigation; multi-sensor data fusion; radar and sonar target tracking; sonar imaging and understanding; MBES bathymetry; ASV; artificial neural networks; geoinformatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Krzysztof Kulpa

E-Mail Website
Guest Editor
Defense and Security Research Center, Institute of Electronic Systems, Warsaw University of Technology (WUT), Nowowiejska 15/19, Warsaw, Poland
Interests: 2D and 3D maneuvering target tracking; maritime patrol radar; low RCS target detection and tracking; noise and passive radars; synthetic aperture radar and ISAR imaging; cognitive radars and EW; airborne passive radars
Special Issues, Collections and Topics in MDPI journals
Dr. Witold Kazimierski

E-Mail Website
Guest Editor
Department of Geoinformatics and Hydrography, Faculty of Navigation, Maritime University of Szczecin, 70-500 Szczecin, Poland
Interests: target tracking; data fusion; maritime radars; spatial analysis; artificial neural networks; mobile cartography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past years, Radar and sonar technology has been at the center of several major developments in remote sensing both in civilian and defense applications. Although radar technology has been known for more than 100 years, it is still developing and it is now implemented in many maritime, air, satellite, and land applications. New technologies such as sparse image reconstruction and multistatic active and passive SAR and ISAR imaging are changing the quality of images and areas of applications. The rapid development of automotive radars in 3D dimensions, able to recognize different objects and assign the risk of collision, is one example of the progress of this technology. In maritime radars, the application of FMCW technology is becoming more and more popular, aside from classical pulse radars. Simultaneously, sonar technology has also been used for dozens of decades, at the beginning only for military solutions but, today, using 3D versions, it is used for many underwater tasks, such as underwater surface imaging, target detections, and tracking, among others. The impact of sonar technologies has been growing, particularly at the beginning of autonomous vehicles era. Recently, the influence of artificial intelligence for radar and sonar image processing and understanding has emerged. Radar and sonar systems are mounted onboard of smart and flexible platforms and also on several types of unmanned vehicles. Both of these technologies focus on remote detection of targets and both may encounter many common scientific challenges. Unfortunately, specialists from the radar and sonar fields do not interact with each other, slowing down progress in both areas.

This Special Issue will report the latest advances and trends in the field of remote sensing for radar and sonar image processing, addressing original developments, new applications, and practical solutions to open questions. The aim is to increase the data and knowledge exchange between those two communities and allow experts from other areas to understand the radar and sonar problems. Topics for this Special Issue include, but are not limited to, the following:

  • Passive and active radar imaging (SAR, ISAR)
  • Passive, bistatic, and multi-static radar imaging
  • 3D radar and 3D sonar imaging
  • Sonar image processing, data reduction, feature extraction, and image understanding
  • Interferometric methods
  • Sparse image reconstruction
  • Automatic target detection and classification
  • Radar sensors design and platform developments
  • Radar and sonar target tracking and anti-collision algorithms and methods
  • Multi-sensor data fusion
  • Synergy between radar, sonar, and other sensors
  • Radar and sonar base autonomous navigation
  • Ground Penetrating Radar application in civil engineering
  • Automotive and maritime radar
  • Radar and sonar surveillance systems
  • Side scan sonar, imaging sonar, chirp sonar, and forward-looking sonar
  • Artificial Intelligence for radar and sonar data processing
Prof. Dr. Andrzej Stateczny
Prof. Dr. Krzysztof Kulpa
Dr. Witold Kazimierski
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

  • radar
  • sonar
  • data fusion
  • sensors design
  • target tracking
  • target imaging
  • image understanding and target recognition.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Published Papers (15 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Other

8 pages, 197 KiB  
Editorial
Radar and Sonar Imaging and Processing (2nd Edition)
by Andrzej Stateczny, Witold Kazimierski and Krzysztof Kulpa
Remote Sens. 2021, 13(22), 4656; https://doi.org/10.3390/rs13224656 - 18 Nov 2021
Cited by 3 | Viewed by 2507
Abstract
The 14 papers (from 29 submitted) published in the Special Issue “Radar and Sonar Imaging Processing (2nd Edition)” highlight a variety of topics related to remote sensing with radar and sonar sensors. The sequence of articles included in the SI deal with a [...] Read more.
The 14 papers (from 29 submitted) published in the Special Issue “Radar and Sonar Imaging Processing (2nd Edition)” highlight a variety of topics related to remote sensing with radar and sonar sensors. The sequence of articles included in the SI deal with a broad profile of aspects of the use of radar and sonar images in line with the latest scientific trends, in which the latest developments in science, including artificial intelligence, were used. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)

Research

Jump to: Editorial, Other

27 pages, 11076 KiB  
Article
Scattering Model-Based Frequency-Hopping RCS Reconstruction Using SPICE Methods
by Yingjun Li, Wenpeng Zhang, Biao Tian, Wenhao Lin and Yongxiang Liu
Remote Sens. 2021, 13(18), 3689; https://doi.org/10.3390/rs13183689 - 15 Sep 2021
Cited by 4 | Viewed by 2035
Abstract
RCS reconstruction is an important way to reduce the measurement time in anechoic chambers and expand the radar original data, which can solve the problems of data scarcity and a high measurement cost. The greedy pursuit, convex relaxation, and sparse Bayesian learning-based sparse [...] Read more.
RCS reconstruction is an important way to reduce the measurement time in anechoic chambers and expand the radar original data, which can solve the problems of data scarcity and a high measurement cost. The greedy pursuit, convex relaxation, and sparse Bayesian learning-based sparse recovery methods can be used for parameter estimation. However, these sparse recovery methods either have problems in solving accuracy or selecting auxiliary parameters, or need to determine the probability distribution of noise in advance. To solve these problems, a non-parametric Sparse Iterative Covariance Estimation (SPICE) algorithm with global convergence property based on the sparse Geometrical Theory of Diffraction (GTD) model (GTD–SPICE) is employed for the first time for RCS reconstruction. Furthermore, an improved coarse-to-fine two-stage SPICE method (DE–GTD–SPICE) based on the Damped Exponential (DE) model and the GTD model (DE–GTD) is proposed to reduce the computational cost. Experimental results show that both the GTD–SPICE method and the DE–GTD–SPICE method are reliable and effective for RCS reconstruction. Specifically, the DE–GTD–SPICE method has a shorter computational time. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Figure 1

24 pages, 27974 KiB  
Article
A Novel Sub-Image Local Area Minimum Entropy Reconstruction Method for HRWS SAR Adaptive Unambiguous Imaging
by Liming Zhou, Xiaoling Zhang, Xu Zhan, Liming Pu, Tianwen Zhang, Jun Shi and Shunjun Wei
Remote Sens. 2021, 13(16), 3115; https://doi.org/10.3390/rs13163115 - 6 Aug 2021
Cited by 7 | Viewed by 2140
Abstract
Multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a vital technique for modern remote sensing. As multichannel SAR systems usually face the problem of azimuth nonuniform sampling resulting in azimuth ambiguity, the conventional reconstruction methods are adopted to obtain the uniformly [...] Read more.
Multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a vital technique for modern remote sensing. As multichannel SAR systems usually face the problem of azimuth nonuniform sampling resulting in azimuth ambiguity, the conventional reconstruction methods are adopted to obtain the uniformly sampled signal. However, various errors, especially amplitude, phase, and baseline errors, always significantly degrade the performance of the reconstruction methods. To solve this problem, in this paper, a novel sub-image local area minimum entropy reconstruction method (SILAMER) is proposed, which has favorable adaptability to the HRWS SAR system with various errors. First, according to the idea of image domain reconstruction, the sub-images are generated by employing the back-projection algorithm. Then, we proposed an estimation algorithm based on sub-image local area minimum entropy to obtain the optimal reconstruction coefficient and the compensation phase, which can greatly improve the estimation efficiency by using a local area of the sub-image as the input for estimation. Finally, the sub-images are weighted by the optimal estimated reconstruction coefficient and calibrated by the compensation phase to obtain the unambiguous reconstruction image. The experimental results verify the effectiveness of the proposed method. Noticeably, the proposed algorithm has two additional advantages, i.e., (1) it can perform well under the condition of low signal-to-noise ratio (SNR), and (2) it is suitable for the curved trajectory SAR reconstruction. The simulations verify these advantages of the proposed method. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Figure 1

22 pages, 19537 KiB  
Article
Focus Improvement of Airborne High-Squint Bistatic SAR Data Using Modified Azimuth NLCS Algorithm Based on Lagrange Inversion Theorem
by Chuang Li, Heng Zhang and Yunkai Deng
Remote Sens. 2021, 13(10), 1916; https://doi.org/10.3390/rs13101916 - 13 May 2021
Cited by 10 | Viewed by 2114
Abstract
In this paper, a modified azimuth nonlinear chirp scaling (NLCS) algorithm is derived for high-squint bistatic synthetic aperture radar (BiSAR) imaging to solve its inherent difficult issues, including the large range cell migration (RCM), azimuth-dependent Doppler parameters, and the sensibility of the higher [...] Read more.
In this paper, a modified azimuth nonlinear chirp scaling (NLCS) algorithm is derived for high-squint bistatic synthetic aperture radar (BiSAR) imaging to solve its inherent difficult issues, including the large range cell migration (RCM), azimuth-dependent Doppler parameters, and the sensibility of the higher order terms. First, using the Lagrange inversion theorem, an accurate spectrum suitable for processing airborne high-squint BiSAR data is introduced. Different from the spectrum that is based on the method of series reversion (MSR), it is allowed to derive the bistatic stationary phase point while retaining the double square root (DSR) of the slant range history. Based the spectrum, a linear RCM correction is used to remove the most of the linear RCM components and mitigate the range-azimuth coupling, and, then, bulk secondary range compression is implemented to compensate the residual RCM and cross-coupling terms. Following this, a modified azimuth NLCS operation is applied to eliminate the azimuth-dependence of Doppler parameters and equalize the azimuth frequency modulation for azimuth compression. The experimental results, with better focusing performance, prove the high accuracy and effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Figure 1

28 pages, 7501 KiB  
Article
Elevation Spatial Variation Analysis and Compensation in GEO SAR Imaging
by Faguang Chang, Dexin Li, Zhen Dong, Yang Huang, Zhihua He and Xing Chen
Remote Sens. 2021, 13(10), 1888; https://doi.org/10.3390/rs13101888 - 12 May 2021
Cited by 8 | Viewed by 2097
Abstract
Due to geosynchronous synthetic aperture radar’s (GEO SAR) high orbit and low relative speed, the integration time reaches up to hundreds of seconds for a fine resolution. The short revisit cycle is essential for remote sensing applications such as disaster monitoring and vegetation [...] Read more.
Due to geosynchronous synthetic aperture radar’s (GEO SAR) high orbit and low relative speed, the integration time reaches up to hundreds of seconds for a fine resolution. The short revisit cycle is essential for remote sensing applications such as disaster monitoring and vegetation measurements. Three-dimensional (3D) scene imaging mode is crucial for long-term observation using GEO SAR. However, this mode will bring a new kind of space-variant error in elevation. In this paper, we focus on the analysis of the elevation space-variant error. First, the decorrelation problems caused by the spatial variation are presented. Second, by combining with the SAR imaging geometry, the elevation spatial variation is decomposed into two-dimensional (2D) space variation of range and azimuth. Third, an imaging algorithm is proposed to solve the 3D space variation and improve the focusing depth. Finally, simulations with dot-matrix targets and distributed targets are performed to validate the imaging method. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Figure 1

28 pages, 41905 KiB  
Article
Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging
by Bokun Tian, Xiaoling Zhang, Liang Li, Ling Pu, Liming Pu, Jun Shi and Shunjun Wei
Remote Sens. 2021, 13(9), 1751; https://doi.org/10.3390/rs13091751 - 30 Apr 2021
Cited by 10 | Viewed by 2034
Abstract
Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a [...] Read more.
Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) is proposed in this paper. The proposed method calculates the maximum marginal likelihood function under the framework of the RVM to obtain the optimal hyper-parameters; the scattering units corresponding to the non-zero optimal hyper-parameters are extracted as the target-areas in the imaging scene. Then, based on the target-areas, we simplify the measurement matrix and conduct sparse imaging. In addition, under low signal to noise ratio (SNR), low sampling rate, or high sparsity, the target-areas cannot always be extracted accurately, which probably contain several elements whose scattering coefficients are too small and closer to 0 compared to other elements. Those elements probably make the diagonal matrix singular and irreversible; the scattering coefficients cannot be estimated correctly. To solve this problem, the inverse matrix of the singular matrix is replaced with the generalized inverse matrix obtained by the truncated singular value decomposition (TSVD) algorithm to estimate the scattering coefficients correctly. Based on the rank of the singular matrix, those elements with small scattering coefficients are extracted and eliminated to obtain more accurate target-areas. Both simulation and experimental results show that the proposed method can improve the computational efficiency and imaging quality of LASAR 3D imaging compared with the state-of-the-art CS-based methods. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Graphical abstract

21 pages, 1999 KiB  
Article
Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar
by Witold Kazimierski and Grzegorz Zaniewicz
Remote Sens. 2021, 13(5), 1014; https://doi.org/10.3390/rs13051014 - 8 Mar 2021
Cited by 32 | Viewed by 3044
Abstract
Target tracking is a process that provides information about targets in a specific area and is one of the key issues affecting the safety of any vehicle navigating in water. The main sensor used for underwater target tracking is sonar, with one of [...] Read more.
Target tracking is a process that provides information about targets in a specific area and is one of the key issues affecting the safety of any vehicle navigating in water. The main sensor used for underwater target tracking is sonar, with one of the most popular configurations being forward looking sonar (FLS). The target tracking state vector is usually estimated with the use of numerical filter algorithms, such as the Kalman filter (KF) and its modification, or the particle filter (PF). This requires the definition of a process model, including process noise, and a measurement model. This study focused on process noise definition. It is usually implemented as Gaussian noise, with a covariance matrix defined by the author. An analytical and empirical analysis was conducted, including a verification of the existing approaches and a survey of the published literature. Additionally, a theoretical analysis of the factors influencing process noise was conducted, which was followed by an empirical verification. The results were discussed, leading to the conclusions. The results of the theoretical analysis were confirmed by the empirical experiment and the results were compared with commonly used values of process noise in underwater target tracking processes. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Graphical abstract

18 pages, 8697 KiB  
Article
Sediment Classification of Acoustic Backscatter Image Based on Stacked Denoising Autoencoder and Modified Extreme Learning Machine
by Ping Zhou, Gang Chen, Mingwei Wang, Jifa Chen and Yizhe Li
Remote Sens. 2020, 12(22), 3762; https://doi.org/10.3390/rs12223762 - 16 Nov 2020
Cited by 6 | Viewed by 2950
Abstract
Acoustic backscatter data are widely applied to study the distribution characteristics of seabed sediments. However, the ghosting and mosaic errors in backscatter images lead to interference information being introduced into the feature extraction process, which is conducted with a convolutional neural network or [...] Read more.
Acoustic backscatter data are widely applied to study the distribution characteristics of seabed sediments. However, the ghosting and mosaic errors in backscatter images lead to interference information being introduced into the feature extraction process, which is conducted with a convolutional neural network or auto encoder. In addition, the performance of the existing classifiers is limited by such incorrect information, meaning it is difficult to achieve fine classification in survey areas. Therefore, we propose a sediment classification method based on the acoustic backscatter image by combining a stacked denoising auto encoder (SDAE) and a modified extreme learning machine (MELM). The SDAE is used to extract the deep-seated sediment features, so that the training network can automatically learn to remove the residual errors from the original image. The MELM model, which integrates weighted estimation, a Parzen window and particle swarm optimization, is applied to weaken the interference of mislabeled samples on the training network and to optimize the random expression of input layer parameters. The experimental results show that the SDAE-MELM method greatly reduces mutual interference between sediment types, while the sediment boundaries are clear and continuous. The reliability and robustness of the proposed method are better than with other approaches, as assessed by the overall classification effect and comprehensive indexes. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Graphical abstract

20 pages, 8566 KiB  
Article
A Novel Horizon Picking Method on Sub-Bottom Profiler Sonar Images
by Shaobo Li, Jianhu Zhao, Hongmei Zhang, Zijun Bi and SiHeng Qu
Remote Sens. 2020, 12(20), 3322; https://doi.org/10.3390/rs12203322 - 12 Oct 2020
Cited by 13 | Viewed by 3118
Abstract
Traditional manual horizon picking is time-consuming and laborious, while automatic picking methods often suffer from the limited scope of their applications and the discontinuity of picked results. In this paper, we propose a novel method for automatic horizon picking from sub-bottom profiles (SBP) [...] Read more.
Traditional manual horizon picking is time-consuming and laborious, while automatic picking methods often suffer from the limited scope of their applications and the discontinuity of picked results. In this paper, we propose a novel method for automatic horizon picking from sub-bottom profiles (SBP) by an improved filtering algorithm. First, a clear and fine SBP image is formed using an intensity transformation method. On this basis, a novel filtering method is proposed by improving the multi-scale enhancement filtering algorithm to obtain clear horizons from an SBP image. The improvement is performed by applying a vertical suppression weighting term based on the form of logistic function, which is constructed by using the eigenvectors from the Hessian matrix. Then, the filtered image is segmented using a threshold method, and the horizon points in the SBP image are picked. After that, a horizon linking method is applied, which uses the horizon directions to refine the picked horizon points. The proposed method has been verified experimentally, and accurate and continuous horizons were obtained. Finally, the proposed method is discussed and some conclusions are drawn. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Graphical abstract

26 pages, 9837 KiB  
Article
DBF Processing in Range-Doppler Domain for MWE SAR Waveform Separation Based on Digital Array-Fed Reflector Antenna
by Shenjing Wang, Yifan Sun, Feng He, Zaoyu Sun, Pengcheng Li and Zhen Dong
Remote Sens. 2020, 12(19), 3161; https://doi.org/10.3390/rs12193161 - 26 Sep 2020
Cited by 7 | Viewed by 3418
Abstract
With the rapid development of the multiple-input multiple-output synthetic aperture radar (MIMO SAR) system, the demands for miniaturization and high gain of antenna are increasing. The digital array-fed reflector antenna has such virtues so that it can play an important role in such [...] Read more.
With the rapid development of the multiple-input multiple-output synthetic aperture radar (MIMO SAR) system, the demands for miniaturization and high gain of antenna are increasing. The digital array-fed reflector antenna has such virtues so that it can play an important role in such system. However, the geometric models and signal models based on a reflector antenna are considerably different from the directly radiating planar antenna. The signal processing for the reflector antenna is more complex and difficult. As a result, the applications of the reflector antenna in SAR system are not as mature as those of the planar antenna. A combination of multidimensional waveform encoding (MWE) technique and digital beamforming (DBF) technology at the receiving end can greatly improve the MIMO SAR system performance, especially ambiguity suppression and waveform separation. This configuration can realize different radar functions and meet multidimensional observation requirements, such as the polarized SAR. Thus, this study combines digital array-fed reflector antenna and the DBF technique in the elevation direction for MWE SAR waveform separation. The echo models for the array-fed reflector antenna and the planar antenna are established based on short-time shift-orthogonal waveforms. In the models, a mismatch in steering vectors is inevitable if DBF processing is continuously performed traditionally in the azimuth-elevation two-dimensional time domain. This mismatch will worsen the waveform separation effect and the image quality. Therefore, we propose a DBF method which is processed in range-Doppler domain. The method enables waveform separation without ambiguity at the receiver. Then, the conventional SAR imaging methods are enabled, and we acquire an ideal SAR image. The simulation results for both point targets and distributed targets prove the effect and feasibility of the proposed DBF method. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Graphical abstract

20 pages, 9263 KiB  
Article
A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising
by Shaobo Li, Jianhu Zhao, Hongmei Zhang, Zijun Bi and Siheng Qu
Remote Sens. 2020, 12(14), 2336; https://doi.org/10.3390/rs12142336 - 21 Jul 2020
Cited by 15 | Viewed by 3243
Abstract
Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining [...] Read more.
Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Graphical abstract

18 pages, 3722 KiB  
Article
A Fast Bistatic ISAR Imaging Approach for Rapidly Spinning Targets via Exploiting SAR Technique
by Zhijun Yang, Dong Li, Xiaoheng Tan, Hongqing Liu and Guisheng Liao
Remote Sens. 2020, 12(13), 2077; https://doi.org/10.3390/rs12132077 - 28 Jun 2020
Cited by 2 | Viewed by 2566
Abstract
Because of the large range of cell migration (RCM) and nonstationary Doppler frequency modulation (DFM) produced by non-cooperative targets with rapid spinning motions, it is difficult to efficiently generate a well-focused bistatic inverse synthetic aperture radar (ISAR) by use of the conventional imaging [...] Read more.
Because of the large range of cell migration (RCM) and nonstationary Doppler frequency modulation (DFM) produced by non-cooperative targets with rapid spinning motions, it is difficult to efficiently generate a well-focused bistatic inverse synthetic aperture radar (ISAR) by use of the conventional imaging algorithms. Utilizing the property of the inherent azimuth spatial invariance in strip-map synthetic aperture radar (SAR) imaging mode, in this work, an efficient bistatic ISAR imaging approach based on circular shift operation in the range-Doppler (RD) domain is proposed. First, echoes of rapidly spinning targets are transformed into the RD domain, whose exact analytical is derived on the basis of the principle of stationary phase (POSP). Second, the RCM is corrected by using an efficient circular shift operation in the RD domain. By doing so, the energies of a scatterer that span multiple range cells are concentrated into the same range cell, and the time-varying DFM can also be compensated along the rotating radius direction. Compared with existing methods, the proposed method has advantages in its computational complexity, avoiding the interpolation and multi-dimensional search operation, and in its satisfactory imaging performance under low signal to noise ratio (SNR) conditions thanks to the two-dimensional coherent integration gain utilized. Finally, several numerical simulations are conducted to show the validity of the proposed algorithm. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Graphical abstract

18 pages, 6151 KiB  
Article
Geospatial Modeling of the Tombolo Phenomenon in Sopot using Integrated Geodetic and Hydrographic Measurement Methods
by Mariusz Specht, Cezary Specht, Janusz Mindykowski, Paweł Dąbrowski, Romuald Maśnicki and Artur Makar
Remote Sens. 2020, 12(4), 737; https://doi.org/10.3390/rs12040737 - 23 Feb 2020
Cited by 35 | Viewed by 4798
Abstract
A tombolo is a narrow belt connecting a mainland with an island lying near to the shore, formed as a result of sand and gravel being deposited by sea currents, most often created as a result of natural phenomena. However, it can also [...] Read more.
A tombolo is a narrow belt connecting a mainland with an island lying near to the shore, formed as a result of sand and gravel being deposited by sea currents, most often created as a result of natural phenomena. However, it can also be caused by human activity, as is the case with the Sopot pier—a town located on the southern coast of the Baltic Sea in northern Poland (φ = 54°26’N, λ = 018°33’E). As a result, the seafloor rises constantly and the shoreline moves towards the sea. Moreover, there is the additional disturbing phenomenon consisting of the rising seafloor sand covering over the waterbody’s vegetation and threatening the city's spa character. Removal of the sand to another place has already been undertaken several times. There is a lack of precise geospatial data about the tombolo’s seafloor course, its size and spatial shape caused by only lowering the seafloor in random places, and the ongoing environmental degradation process. This article presents the results of extensive and integrated geodetic and hydrographic measurements, the purpose of which was to make a 3D model of the phenomena developing in Sopot. The measurements will help determine the size and speed of the geospatial changes. Most of the modern geodetic and hydrographic methods were used in the study of these phenomena. For the construction of the land part of geospatial model, the following were used: photos from the photogrammetric flight pass (unmanned aerial vehicle—UAV), laser scanning of the beach and piers, and satellite orthophotomaps for analysis of the coastline changes. In the sea part, bathymetric measurements were carried out with an unmanned surface vehicle (USV). Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Graphical abstract

18 pages, 8453 KiB  
Article
Results from Developments in the Use of a Scanning Sonar to Support Diving Operations from a Rescue Ship
by Artur Grządziel
Remote Sens. 2020, 12(4), 693; https://doi.org/10.3390/rs12040693 - 20 Feb 2020
Cited by 12 | Viewed by 5700
Abstract
In recent years, widespread use of scanning sonars for acoustic imaging of the seabed surface can be observed. These types of sonars are mainly used with tripods or special booms, or are mounted onboard remotely operated or unmanned vehicles. Typical scanning sonar applications [...] Read more.
In recent years, widespread use of scanning sonars for acoustic imaging of the seabed surface can be observed. These types of sonars are mainly used with tripods or special booms, or are mounted onboard remotely operated or unmanned vehicles. Typical scanning sonar applications include search and recovery operations, imaging of underwater infrastructure, and scour monitoring. The use of these sonars is often limited to shallow waters. Diver teams or underwater remotely operated vehicles (ROV) are commonly used to inspect shipwrecks, port wharfs, and ship hulls. However, reduced underwater visibility, submerged debris, and extreme water depths can limit divers’ capabilities. In this paper, a novel, nonstandard technique for use of a scanning sonar is proposed. The new application for scanning sonar technology is a practical solution developed on the Polish Navy’s search and rescue ship “Lech.” To verify the effectiveness of the proposed technique, the author took part in four different studies carried out in the southeastern Baltic Sea. The tests were performed using the MS 1000 scanning sonar. The results demonstrate that the proposed technique has the potential to provide detailed sonar images of the seabed and underwater objects before the descent of divers. The divers get acquainted with the underwater situation, which undoubtedly increases the safety of the entire operation. Scanning sonars are unlikely to completely replace the work of divers but may reduce the number and duration of dives. The sonar use technique turned out to be useful when rescuing a crew of a submarine that crashed and settled on the sea bottom as part of a naval exercise. The sonar data obtained during four experimental tests performed in the Baltic Sea prove the validity, usefulness, and significance of the proposed technique, especially from the standpoint of safety of underwater work. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Graphical abstract

Other

Jump to: Editorial, Research

18 pages, 23703 KiB  
Technical Note
Elevation Spatial Variation Error Compensation in Complex Scene and Elevation Inversion by Autofocus Method in GEO SAR
by Faguang Chang, Dexin Li, Zhen Dong, Yang Huang and Zhihua He
Remote Sens. 2021, 13(15), 2916; https://doi.org/10.3390/rs13152916 - 24 Jul 2021
Cited by 3 | Viewed by 1895
Abstract
Due to the high altitude of geosynchronous synthetic aperture radar (GEO SAR), its synthetic aperture time can reach up to several hundred seconds, and its revisit cycle is very short, which makes it of great application worth in the remote sensing field, such [...] Read more.
Due to the high altitude of geosynchronous synthetic aperture radar (GEO SAR), its synthetic aperture time can reach up to several hundred seconds, and its revisit cycle is very short, which makes it of great application worth in the remote sensing field, such as in disaster monitoring and vegetation measurements. However, because of the elevation of the target, elevation spatial variation error is caused in the GEO SAR imaging. In this paper, we focus on the compensation of the elevation space-variant error in the fast variant part with the autofocus method and utilize the error to carry out elevation inversing in complex scenes. For a complex scene, it can be broken down into a slow variant slope and the remaining fast variant part. First, the phase error caused by the elevation spatial variation is analyzed. Second, the spatial variant error caused by the slowly variant slope is compensated with the improved imaging algorithm. The error caused by the remaining fast variable part is the focus of this paper. We propose a block map-drift phase gradient autofocus (block-MD-PGA) algorithm to compensate for the random phase error part. By dividing sub-blocks reasonably, the elevation spatial variant error is compensated for by an autofocus algorithm in each sub-block. Because the errors of different elevations are diverse, the proposed algorithm is suitable for the scene where the target elevations are almost the same after the sub-blocks are divided. Third, the phase error obtained by the autofocus method is used to inverse the target elevation. Finally, simulations with dot-matrix targets and targets based on the high-resolution TerraSAR-X image verify the excellent effect of the proposed method and the accuracy of the elevation inversion. Full article
(This article belongs to the Special Issue 2nd Edition Radar and Sonar Imaging and Processing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-15
Back to TopTop