Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorJacob, Alexander W.en_US
dc.contributor.authorNotarnicola, Claudiaen_US
dc.contributor.authorSuresh, Gopikaen_US
dc.contributor.authorAntropov, Olegen_US
dc.contributor.authorGe, Shaojiaen_US
dc.contributor.authorPraks, Jaanen_US
dc.contributor.authorBan, Yifangen_US
dc.contributor.authorPottier, Ericen_US
dc.contributor.authorMallorqui Franquet, Jordi Joanen_US
dc.contributor.authorDuro, Javieren_US
dc.contributor.authorEngdahl, Marcus E.en_US
dc.contributor.authorVicente-Guijalba, Fernandoen_US
dc.contributor.authorLopez-Martinez, Carlosen_US
dc.contributor.authorLopez-Sanchez, Juan M.en_US
dc.contributor.authorLitzinger, Mariusen_US
dc.contributor.authorKristen, Haralden_US
dc.contributor.authorMestre-Quereda, Alejandroen_US
dc.contributor.authorZiolkowski, Dariuszen_US
dc.contributor.authorLavalle, Marcoen_US
dc.contributor.departmentDepartment of Electronics and Nanoengineeringen
dc.contributor.groupauthorJaan Praks Groupen
dc.contributor.organizationEURAC Researchen_US
dc.contributor.organizationNanjing University of Science and Technologyen_US
dc.contributor.organizationFederal Agency for Cartography and Geodesyen_US
dc.contributor.organizationKTH Royal Institute of Technologyen_US
dc.contributor.organizationUniversité de Rennes 1en_US
dc.contributor.organizationPolytechnic University of Cataloniaen_US
dc.contributor.organizationDares Technologyen_US
dc.contributor.organizationESRIN - ESA Centre for Earth Observationen_US
dc.contributor.organizationUniversity of Alicanteen_US
dc.contributor.organizationInstitute of Geodesy and Cartographyen_US
dc.contributor.organizationJet Propulsion Laboratoryen_US
dc.contributor.organizationVTT Technical Research Centre of Finlanden_US
dc.date.accessioned2020-04-03T09:48:42Z
dc.date.available2020-04-03T09:48:42Z
dc.date.issued2020-01-01en_US
dc.description.abstractThis article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain - interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Doñana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.en
dc.description.versionPeer revieweden
dc.format.extent18
dc.format.extent535-552
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationJacob, A W, Notarnicola, C, Suresh, G, Antropov, O, Ge, S, Praks, J, Ban, Y, Pottier, E, Mallorqui Franquet, J J, Duro, J, Engdahl, M E, Vicente-Guijalba, F, Lopez-Martinez, C, Lopez-Sanchez, J M, Litzinger, M, Kristen, H, Mestre-Quereda, A, Ziolkowski, D & Lavalle, M 2020, ' Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers ', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, 8966616, pp. 535-552 . https://doi.org/10.1109/JSTARS.2019.2958847en
dc.identifier.doi10.1109/JSTARS.2019.2958847en_US
dc.identifier.issn1939-1404
dc.identifier.issn2151-1535
dc.identifier.otherPURE UUID: 606ccad0-08e6-4ed3-9b86-f129b84c9bf7en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/606ccad0-08e6-4ed3-9b86-f129b84c9bf7en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85079791755&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/41866245/ELEC_Jacob_Sentinel_1_IEEEJoSTiAEO.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/43655
dc.identifier.urnURN:NBN:fi:aalto-202004032685
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartofseriesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensingen
dc.relation.ispartofseriesVolume 13en
dc.rightsopenAccessen
dc.subject.keywordCopernicusen_US
dc.subject.keywordinterferometric coherenceen_US
dc.subject.keywordland cover mappingen_US
dc.subject.keywordSentinel-1en_US
dc.subject.keywordsynthetic aperture radar (SAR)en_US
dc.titleSentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiersen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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