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: closed (20 February 2022) | Viewed by 4818
Special Issue Editors
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
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
Interests: synthetic aperture radar (SAR); SAR interferometry; changing detection; despeckling; denoising; edge detection; SAR tomography
Special Issues, Collections and Topics in MDPI journals
Interests: persistent scatterer interferometry; SAR tomography; distributed scatterer interferometry and their applications for urban infrastructural deformation monitoring and geohazard monitoring
Interests: data fusion; despeckling; super-resolution; pansharpening; deep learning
Special Issues, Collections and Topics in MDPI journals
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
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Keywords
- SAR
- InSAR
- deep learning
- CNN
- signal processing
- data processing
- artificial intelligence
