Show simple item record

dc.contributor.authorMirmazloumi, S. Mohammad
dc.contributor.authorGambin, Angel Fernandez
dc.contributor.authorPalamà, Riccardo
dc.contributor.authorCrosetto, Michele
dc.contributor.authorWassie, Yismaw
dc.contributor.authorNavarro, José A.
dc.contributor.authorBarra, Anna
dc.contributor.authorMonserrat, Oriol
dc.date.accessioned2023-05-10T09:14:09Z
dc.date.available2023-05-10T09:14:09Z
dc.date.created2022-11-09T10:13:15Z
dc.date.issued2022
dc.identifier.citationRemote Sensing. 2022, 14 (15), .en_US
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/11250/3067447
dc.description.abstractThe increasing availability of Synthetic Aperture Radar (SAR) images facilitates the generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deformation identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.ispartofseriesRemote Sensing;
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleSupervised Machine Learning Algorithms for Ground Motion Time Series Classification from InSAR Dataen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doihttp://dx.doi.org/10.3390/rs14153821
dc.identifier.cristin2071026
dc.source.journalRemote Sensingen_US
dc.source.volume14en_US
dc.source.issue15en_US
dc.source.pagenumber1-20en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal