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InSAR Displacement Time Series Mining: A Machine Learning Approach

Ansari, Homa and Russwurm, Marc and Ali, Syed Mohsin and Montazeri, Sina and Parizzi, Alessandro and Zhu, Xiao Xiang (2021) InSAR Displacement Time Series Mining: A Machine Learning Approach. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1-4. IGARSS 2021, 12.-16. July 2021, Brussels, Belgium + Virtual.

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Interferometric Synthetic Aperture Radar (InSAR)-derivedsurface displacement time series enable a wide range of ap-plications from urban structural monitoring to geohazardassessment.With systematic data acquisitions becomingthe new norm for SAR missions, millions of time series arecontinuously generated. Machine Learning provides a frame-work for the efficient mining of such big data. Here, we focuson unsupervised mining of the data via clustering the similartemporal patterns and data-driven displacement signal re-construction from the InSAR time series. We propose a deepLong Short Term Memory (LSTM) autoencoder model whichcan exploit temporal relations in contrast to the commonlyused shallow learning methods, such as Uniform ManifoldApproximation and Projection (UMAP). We also modify theloss function to allow the quantification of uncertainties inthe time series data. The two approaches are applied to theLazufre Volcanic Complex located at the central volcaniczone of the Andes and thereby compared.

Item URL in elib:https://elib.dlr.de/142311/
Document Type:Conference or Workshop Item (Speech)
Title:InSAR Displacement Time Series Mining: A Machine Learning Approach
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Ansari, HomaHoma.Ansari (at) dlr.dehttps://orcid.org/0000-0002-4549-2497
Ali, Syed MohsinSyed.Ali (at) dlr.deUNSPECIFIED
Montazeri, SinaSina.Montazeri (at) dlr.dehttps://orcid.org/0000-0002-6732-1381
Parizzi, AlessandroAlessandro.Parizzi (at) dlr.dehttps://orcid.org/0000-0002-5651-8218
Zhu, Xiao Xiangxiao.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Date:July 2021
Journal or Publication Title:International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-4
Keywords:Time Series, Autoencoders, LSTM, Deep Learning, Shallow Learning, InSAR, Deformation
Event Title:IGARSS 2021
Event Location:Brussels, Belgium + Virtual
Event Type:international Conference
Event Dates:12.-16. July 2021
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 - Artificial Intelligence, R - SAR methods
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Ansari, Homa
Deposited On:10 Jun 2021 13:50
Last Modified:09 Aug 2021 13:17

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