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A CNN-Based Coherence-Driven Approach for InSAR Phase Unwrapping

Sica, Francescopaolo and Calvanese, Francesco and Scarpa, Giuseppe and Rizzoli, Paola (2020) A CNN-Based Coherence-Driven Approach for InSAR Phase Unwrapping. IEEE Geoscience and Remote Sensing Letters. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/lgrs.2020.3029565. ISSN 1545-598X.

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Phase unwrapping (PU) is among the most critical tasks in synthetic aperture radar (SAR) interferometry (InSAR). Due to the presence of noise, the interferogram usually presents phase inconsistencies, also called residues, which imply a nonunivocal solution. This work investigates the PU problem from a semantic segmentation perspective by exploiting convolutional neural network (CNN) models. In particular, by exploiting a popular deep-learning architecture, we introduce the interferometric coherence as an input feature and analyze the performance increase against classical methods. For the network training, we generate a variegated data set by introducing a controlled number of phase residues, and considering both synthetic and real InSAR data. Eventually, we compare the proposed method to state-of-the-art algorithms on synthetic and real InSAR data taken from the TanDEM-X mission, obtaining encouraging results.

Item URL in elib:https://elib.dlr.de/140351/
Document Type:Article
Title:A CNN-Based Coherence-Driven Approach for InSAR Phase Unwrapping
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Sica, FrancescopaoloUNSPECIFIEDhttps://orcid.org/0000-0003-1593-1492UNSPECIFIED
Calvanese, FrancescoUniversitá di Napoli Federico IIUNSPECIFIEDUNSPECIFIED
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732UNSPECIFIED
Date:21 October 2020
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Deep Learning, SAR interferometry, Phase Unwrapping
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - SAR methods
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute
Microwaves and Radar Institute > SAR Technology
Microwaves and Radar Institute > Spaceborne SAR Systems
Deposited By: Sica, Dr. Francescopaolo
Deposited On:12 Jan 2021 17:48
Last Modified:12 Dec 2022 09:58

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