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Special Issue "Deep Learning for InSAR Signal and Data Processing"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: 20 February 2022.

Special Issue Editors

Prof. Dr. Vito Pascazio
E-Mail Website
Guest Editor
Department of Engineering, Università di Napoli “Parthenope”, Centro Direzionale Isola C4, 80143 Napoli, Italy
Interests: synthetic aperture radar (SAR) image processing; SAR interferometry and tomography; ground-based SAR; microwave tomographic image reconstruction; ground-penetrating radars; biomedical image processing; magnetic resonance imaging; image processing; image compression; compressive sensing; linear and nonlinear statistical signal processing; Markov random field
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yong Wang
E-Mail Website
Guest Editor
Department of Geography, Planning, and Environment, East Carolina University, Greenville, NC 27858, USA
Interests: investigation of scale and scale effect on synthetic aperture radar (SAR) to urban target delineation; evaluation of surface deformation using interferometric SAR (InSAR) techniques; removal of thin clouds in optical imagery; mapping of flooding using geospatial datasets
Special Issues, Collections and Topics in MDPI journals
Dr. Giampaolo Ferraioli
E-Mail Website
Guest Editor
Department of Science and Technology, University of Naples “Parthenope”, Centro Direzionale Isola C4, 80143 Naples, Italy
Interests: synthetic aperture radar (SAR); SAR interferometry; changing detection; despeckling; denoising; edge detection; SAR tomography
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Peifeng Ma
E-Mail Website
Guest Editor
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
Interests: persistent scatterer interferometry; SAR tomography; distributed scatterer interferometry and their applications for urban infrastructural deformation monitoring and geohazard monitoring
Dr. Sergio Vitale
E-Mail Website
Guest Editor
Department of Engineering, University of Napoli Parthenope, Centro Direzionale di Napoli, Is. C4, 80143 Naples, Italy
Interests: SAR despeckling; deep learning; SAR tomography; pansharpening; remote sensing; image processing
Dr. Lifan Zhou
E-Mail Website
Guest Editor
School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou 215500, China
Interests: phase unwrapping; algorithm design; machine learning; interferometric synthetic aperture radar signal processing and applications

Special Issue Information

Dear Colleagues,

InSAR technology has been widely applied to digital elevation model (DEM) generation and geo-hazard deformation analysis. As an ill-posed problem, the accuracy of the InSAR product is sensitive to the selection of parameters and processing approaches with rapid ground deformation or topographic changes. It requires that the InSAR signal processing practitioners be well-experienced, which is unfavorable to the generalization and commercialization of InSAR. Today, deep learning provides a new data-driven framework for accumulating experience. Moreover, deep learning techniques and a flood of valuable data coming from different InSAR sensors allow us to enable the learning-based “data model” outside of the traditional ones, which will act as a new discovery agent to investigate and explore previously intractable or inaccessible problems. This Special Issue aims to invite contributions on the latest developments and advances of the learning algorithms and frameworks on InSAR signal processing and applications.

Topics To Be Covered

  • Learning-based approaches on InSAR signal processing chain, e.g., denoising and phase unwrapping
  • Learning algorithms and models of InSAR data for Earth remote sensing (supervised/weakly supervised/unsupervised)
  • Fusion framework of the datasets from disparate InSAR systems
  • A comparative study of the existing learning approaches of InSAR datasets

Prof. Dr. Vito Pascazio
Prof. Dr. Yong Wang
Dr. Giampaolo Ferraioli
Prof. Dr. Peifeng Ma
Dr. Sergio Vitale
Dr. Lifan Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be 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. Sensors 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 2200 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
  • InSAR
  • deep learning
  • CNN
  • signal processing
  • data processing
  • artificial intelligence

Published Papers

This special issue is now open for submission.
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