Neural-network pattern recognition experiments toward a full-automatic detection of anomalies in InSAR time-series of surface deformation

Milillo, Pietro and Sacco, Gianfranco and Di Martire, Diego and Hook, Hua (2021) Neural-network pattern recognition experiments toward a full-automatic detection of anomalies in InSAR time-series of surface deformation. Frontiers in Earth Science. Frontiers Media S.A.. doi: 10.3389/feart.2021.728643. ISSN 2296-6463.

Full text not available from this repository.

Official URL: https://www.frontiersin.org/articles/10.3389/feart.2021.728643/abstract

Item URL in elib:

https://elib.dlr.de/146456/

Document Type:

Article

Title:

Neural-network pattern recognition experiments toward a full-automatic detection of anomalies in InSAR time-series of surface deformation

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD
Milillo, Pietro	pieto.milillo (at) dlr.de	UNSPECIFIED
Sacco, Gianfranco	Gianfranco.Sacco (at) jpl.nasa.gov	UNSPECIFIED
Di Martire, Diego	diego.dimartire (at) unina.it	UNSPECIFIED
Hook, Hua	hook.hua (at) jpl.nasa.gov	UNSPECIFIED

Date:

November 2021

Journal or Publication Title:

Frontiers in Earth Science

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

Yes

In SCOPUS:

Yes

In ISI Web of Science:

Yes

DOI:

10.3389/feart.2021.728643

Publisher:

Frontiers Media S.A.

ISSN:

2296-6463

Status:

Published

Keywords:

Deep Learning, InSAR, deformations

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 - Support TerraSAR-X/TanDEM-X operations

Location:

Oberpfaffenhofen

Institutes and Institutions:

Microwaves and Radar Institute
Microwaves and Radar Institute > Spaceborne SAR Systems

Deposited By:

Rizzoli, Paola

Deposited On:

30 Nov 2021 14:14

Last Modified:

20 Dec 2021 18:32

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