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Remote Sensing
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Synthetic Aperture Radar (SAR)—New Techniques, Missions and Applications
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Synthetic Aperture Radar (SAR)—New Techniques, Missions and Applications

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 9004

Special Issue Editors

Dr. Matthias Weiß

E-Mail Website
Guest Editor
Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, 53343 Wachtberg, Germany
Interests: multistatic radar; waveform design; joint radar–communication; RadCom; synchronization; simultaneous transmit and receive (STAR)
Special Issues, Collections and Topics in MDPI journals
Dr. Gianfranco Fornaro

E-Mail Website
Guest Editor
Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy (CNR), 80124 Neaples, Italy
Interests: SAR; interferometric and tomographic SAR; inverse SAR
Special Issues, Collections and Topics in MDPI journals
Dr. Scott Hensley

E-Mail Website
Guest Editor
Jet Propulsion Lab, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
Interests: radar imaging techniques; ISAR; interferometric ISAR (InISAR); radar polarimetry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) is a well-established remote sensing technique that enables the acquisition of high-resolution images of surfaces, independent of sunlight illumination and weather conditions. SAR has proven to be a unique information source for a large number of applications, ranging from environmental studies to disaster monitoring and reconnaissance. The coherent combination of multiple SAR images makes it possible to generate advanced information products like large-scale deformation maps, digital elevation models, or even 3D tomograms of semi-transparent volume scatters. The increased imaging capabilities of next-generation SAR sensors will further enhance their application spectrum, and will make them an ideal tool to regularly monitor the Earth system and its intricate dynamics.

The call for this Special Issue is associated with the 13th European Conference on Synthetic Aperture Radar (EUSAR), organized as a virtual conference to be held in April 2021 (www.eusar.de). EUSAR is the largest conference worldwide entirely dedicated to the development of synthetic aperture radar technologies, techniques, and their remote sensing applications. Over the past 20 years, EUSAR has established an international forum that brings together engineers and scientists to exchange information on the latest developments of SAR-related topics.

The objective of the Special Issue is to select outstanding contributions on recent advances in the field of synthetic aperture radar. The call is open to all researchers. EUSAR attendees are encouraged to submit an extended version of their conference paper, which should include more detailed derivations, analyses, and experimental results.

Contributions to this Special Issue are welcome on the following topics:

  • Current and future airborne and spaceborne SAR systems and missions;
  • New SAR applications, products, and information retrieval algorithms;
  • Innovative SAR sensors, concepts, techniques, and modes;
  • Advances in ground-based and inverse SAR;
  • Bistatic, multistatic, and passive SAR;
  • SAR calibration, validation, and verification;
  • SAR polarimetry, interferometry, tomography, and holography;
  • Advanced SAR signal processing techniques;
  • Digital beamforming, GMTI, and MIMO-SAR;
  • SAR data evaluation and modeling.

Dr. Matthias Weiß
Dr. Gianfranco Fornaro
Dr. Scott Hensley
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

  • Synthetic aperture radar (SAR)
  • SAR systems, sensors, and missions
  • SAR technology and calibration
  • Digital beamforming
  • Inverse SAR (ISAR)
  • Distributed SAR systems and missions
  • Advanced SAR modes and techniques
  • SAR components and subsystems
  • SAR calibration and verification
  • SAR signal processing, motion compensation, and geocoding
  • MTI, GMTI, and STAPC5 interferometry (cross-track, along-track, differential, PS, ... )
  • Tomography, holography and 4-D SAR
  • SAR data evaluation and modeling
  • SAR applications

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Published Papers (2 papers)

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20 pages, 15993 KiB  
Article
Adversarial Attack for SAR Target Recognition Based on UNet-Generative Adversarial Network
by Chuan Du and Lei Zhang
Remote Sens. 2021, 13(21), 4358; https://doi.org/10.3390/rs13214358 - 29 Oct 2021
Cited by 24 | Viewed by 3612
Abstract
Some recent articles have revealed that synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep learning are vulnerable to the attacks of adversarial examples and cause security problems. The adversarial attack can make a deep convolutional neural network (CNN)-based SAR-ATR system [...] Read more.
Some recent articles have revealed that synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep learning are vulnerable to the attacks of adversarial examples and cause security problems. The adversarial attack can make a deep convolutional neural network (CNN)-based SAR-ATR system output the intended wrong label predictions by adding small adversarial perturbations to the SAR images. The existing optimization-based adversarial attack methods generate adversarial examples by minimizing the mean-squared reconstruction error, causing smooth target edge and blurry weak scattering centers in SAR images. In this paper, we build a UNet-generative adversarial network (GAN) to refine the generation of the SAR-ATR models’ adversarial examples. The UNet learns the separable features of the targets and generates the adversarial examples of SAR images. The GAN makes the generated adversarial examples approximate to real SAR images (with sharp target edge and explicit weak scattering centers) and improves the generation efficiency. We carry out abundant experiments using the proposed adversarial attack algorithm to fool the SAR-ATR models based on several advanced CNNs, which are trained on the measured SAR images of the ground vehicle targets. The quantitative and qualitative results demonstrate the high-quality adversarial example generation and excellent attack effectiveness and efficiency improvement. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR)—New Techniques, Missions and Applications)
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Other

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17 pages, 42603 KiB  
Technical Note
Enhancing Coherence Images for Coherent Change Detection: An Example on Vehicle Tracks in Airborne SAR Images
by Horst Hammer, Silvia Kuny and Antje Thiele
Remote Sens. 2021, 13(24), 5010; https://doi.org/10.3390/rs13245010 - 9 Dec 2021
Cited by 7 | Viewed by 3876
Abstract
In Synthetic Aperture Radar (SAR) interferometry, one of the most widely used measures for the quality of the interferometric phase is coherence. However, in favorable conditions coherence can also be used to detect subtle changes on the ground, which are not visible in [...] Read more.
In Synthetic Aperture Radar (SAR) interferometry, one of the most widely used measures for the quality of the interferometric phase is coherence. However, in favorable conditions coherence can also be used to detect subtle changes on the ground, which are not visible in the amplitude images. For such applications, i.e., coherent change detection, it is important to have a good contrast between the unchanged (high-coherence) parts of the scene and the changed (low-coherence) parts. In this paper, an algorithm is introduced that aims at enhancing this contrast. The enhancement is achieved by a combination of careful filtering of the amplitude images and the interferometric phase image. The algorithm is applied to an airborne interferometric SAR image pair recorded by the SmartRadar experimental sensor of Hensoldt Sensors GmbH. The data were recorded during a measurement campaign over the Bann B installations of POLYGONE Range in southern Rhineland-Palatinate (Germany), with a time gap of approximately four hours between the overflights. In-between the overflights, several vehicles were moved on the site and the goal of this work is to enhance the coherence image such that the tracks of these vehicles can be detected as completely as possible in an automated way. Several coherence estimation schemes found in the literature are explored for the enhancement, as well as several commonly used speckle filters. The results of these filtering steps are evaluated visually and quantitatively, showing that the mean gray-level difference between the low-coherence tracks and their high-coherence surroundings could be enhanced by at least 28%. Line extraction is then applied to the best enhancement. The results show that the tracks can be detected much more completely using the coherence contrast enhancement scheme proposed in this paper. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR)—New Techniques, Missions and Applications)
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