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Remote Sensing
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Advances in SAR-Based Monitoring Systems: System Concepts and Data Processing
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Advances in SAR-Based Monitoring Systems: System Concepts and Data 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 (31 October 2021) | Viewed by 11481

Special Issue Editors

Dr. Maria Daniela Graziano

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Naples “Federico II”, Napoli, Italy
Interests: distributed SAR; maritime surveillance; SAR image processing; satellite constellation analysis and design
Dr. Marco D’Errico

E-Mail Website
Guest Editor
Department of Engineering, University of Campania “Luigi Vanvitelli”, Caserta, Italy
Interests: maritime surveillance; SAR systems; distributed space missions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

SAR-based satellite systems are strongly increasing our capacity to monitor a huge amount of events and phenomena. A big effort has been focused on developing monitoring systems for floods and landslides as well as for tracking icebergs and oil spills. In addition, security-related applications have been investigated, such as the surveillance of maritime traffic of uncollaborative vessels. Not only data processing-related aspects have been faced, but, over the last decade, innovative system concepts have been proposed for enabling performance unreachable by the existing systems.  

This Special Issue focuses on new approaches for the SAR-based monitoring systems. Authors are encouraged to submit contributions on technological and scientific advance concepts in the ambit of image processing and system concept in an operational perspective. As an example, the development of distributed-SAR satellite systems for applications of high-resolution and swath implementation as well as for the improvement of imaging/tracking performance is of interest of the Special Issue. Additionally, original techniques for processing of existing SAR images and interferometric applications are in line with the scope of this Issue. Finally, the integration of SAR data with different sources of information is considered an added value of the future SAR-based monitoring systems.          


Dr. Maria Daniela Graziano
Dr. Marco D’Errico
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

  • SAR image processing
  • SAR-based monitoring system
  • Distributed SAR system
  • Multistatic SAR system
  • SAR Interferometry
  • SAR processing simulation

Published Papers (4 papers)

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Research

22 pages, 9666 KiB  
Article
Azimuth Multichannel Reconstruction Based on Advanced Hyperbolic Range Equation
by Wei Xu, Ruibo Li, Chonghua Fang, Pingping Huang, Weixian Tan and Yaolong Qi
Remote Sens. 2021, 13(22), 4705; https://doi.org/10.3390/rs13224705 - 21 Nov 2021
Cited by 5 | Viewed by 1811
Abstract
To acquire high-resolution wide-swath (HRWS) imaging capacity, the displaced phase center multichannel azimuth beam (DPCMAB) technology is usually adopted in spaceborne synthetic aperture radar (SAR), while multichannel reconstruction must be carried out before imaging process due to azimuth nonuniform sampling. Up to now, [...] Read more.
To acquire high-resolution wide-swath (HRWS) imaging capacity, the displaced phase center multichannel azimuth beam (DPCMAB) technology is usually adopted in spaceborne synthetic aperture radar (SAR), while multichannel reconstruction must be carried out before imaging process due to azimuth nonuniform sampling. Up to now, almost all azimuth multichannel reconstruction algorithms have been mainly based on conventional hyperbolic range equation (CHRE), but the accuracy of the CHRE model is usually not suitable for the HRWS mode, especially for high resolution and large squint observation cases. In this study, the azimuth multichannel signal model based on the advanced hyperbolic range equation (AHRE) is established and analyzed. The major difference between multichannel signal models based on CHRE and AHRE is the additional time-varying phase error between azimuth channels. The time-varying phase error is small and can be ignored in the monostatic DPCMAB SAR system, but it must be considered and compensated in the distributed DPCMAB SAR system. In addition to the time-varying phase error, additional Doppler spectrum shift and extended Doppler bandwidth should be considered in the squint case during azimuth multichannel reconstruction. The azimuth multichannel reconstruction algorithm based on AHRE is proposed in this paper. Before multichannel reconstruction and combination, time-varying phase errors between azimuth channels were first compensated, and the range-frequency-dependent de-skewing function was derived to remove the two-dimension (2D) spectrum tilt to avoid azimuth under-sampling. Then, azimuth multichannel data were reconstructed according to the azimuth multichannel impulse response based on AHRE. Finally, the range-frequency dependent re-skewing function was introduced to recover the tilted 2D spectrum. Simulation results on both point and distributed targets validated the proposed azimuth multichannel reconstruction approach. Full article
(This article belongs to the Special Issue Advances in SAR-Based Monitoring Systems: System Concepts and Data Processing)
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22 pages, 3964 KiB  
Article
Unsupervised Multitemporal Building Change Detection Framework Based on Cosegmentation Using Time-Series SAR
by Kaiyu Zhang, Xikai Fu, Xiaolei Lv and Jili Yuan
Remote Sens. 2021, 13(3), 471; https://doi.org/10.3390/rs13030471 - 29 Jan 2021
Cited by 10 | Viewed by 2396
Abstract
Building change detection using remote sensing images is essential for various applications such as urban management and marketing planning. However, most change detection approaches can only detect the intensity or type of change. The aim of this study is to dig for more [...] Read more.
Building change detection using remote sensing images is essential for various applications such as urban management and marketing planning. However, most change detection approaches can only detect the intensity or type of change. The aim of this study is to dig for more change information from time-series synthetic aperture radar (SAR) images, such as the change frequency and the change moments. This paper proposes a novel multitemporal building change detection framework that can generate change frequency map (CFM) and change moment maps (CMMs) from multitemporal SAR images. We first give definitions of CFM and CMMs. Then we generate change feature using four proposed generators. After that, a new cosegmentation method combining raw images and change feature is proposed to divide time-series images into changed and unchanged areas separately. Secondly, the proposed cosegmentation and the morphological building index (MBI) are combined to extract changed building objects. Then, the logical conjunction between the cosegmentation results and the binarized MBI is performed to recognize every moment of change. In the post-processing step, we use fragment removal to increase accuracy. Finally, we propose a novel accuracy assessment index for CFM. We call this index average change difference (ACD). Compared to the traditional multitemporal change detection methods, our method outperforms other approaches in terms of both qualitative results and quantitative indices of ACD using two TerraSAR-X datasets. The experiments show that the proposed method is effective in generating CFM and CMMs. Full article
(This article belongs to the Special Issue Advances in SAR-Based Monitoring Systems: System Concepts and Data Processing)
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26 pages, 4210 KiB  
Article
Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR
by Xiaqing Yang, Jun Shi, Yuanyuan Zhou, Chen Wang, Yao Hu, Xiaoling Zhang and Shunjun Wei
Remote Sens. 2020, 12(18), 3083; https://doi.org/10.3390/rs12183083 - 20 Sep 2020
Cited by 28 | Viewed by 4386
Abstract
Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach [...] Read more.
Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP. Full article
(This article belongs to the Special Issue Advances in SAR-Based Monitoring Systems: System Concepts and Data Processing)
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20 pages, 2332 KiB  
Article
An Improved Frequency-Domain Image Formation Algorithm for Mini-UAV-Based Forward-Looking Spotlight BiSAR Systems
by Tao Zeng, Zhanze Wang, Feifeng Liu and Chenghao Wang
Remote Sens. 2020, 12(17), 2680; https://doi.org/10.3390/rs12172680 - 19 Aug 2020
Cited by 8 | Viewed by 2261
Abstract
Mini-unmanned aerial vehicle (UAV)-based bistatic forward-looking synthetic aperture radar (SAR) (mini-UAV-based BFSAR) is much more attractive than the monostatic one because of the flexibility of the system geometry selection as well as its simplicity of system operation, especially with the mini-UAV platform. However, [...] Read more.
Mini-unmanned aerial vehicle (UAV)-based bistatic forward-looking synthetic aperture radar (SAR) (mini-UAV-based BFSAR) is much more attractive than the monostatic one because of the flexibility of the system geometry selection as well as its simplicity of system operation, especially with the mini-UAV platform. However, the trajectory of the mini-UAV needs to be accurately modeled since it is very sensitive to the external environment, and the forward-looking configuration results in more severe spatial variance in image formation processing. In the paper, an improved frequency-domain imaging algorithm based on a very accurate slant range model is proposed for mini-UAV-based BFSAR with spotlight illumination. First, a more accurate slant range expression considering the motion characteristics of the UAV and bistatic spotlight configuration is re-derived. Second, a new range nonlinear chirp scaling (NLCS) operator was derived based on the accurate bistatic slant range model. Third, an improved azimuth NLCS operator in the Doppler frequency domain was established for the spotlight illumination of the transmitter and receiver in mini-UAV based BFSAR systems. Finally, the proposed algorithm is validated by both simulations and real datasets. Full article
(This article belongs to the Special Issue Advances in SAR-Based Monitoring Systems: System Concepts and Data Processing)
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