Comparative Study of FDA and Time Series Approaches for Seabed Classification from Acoustic Curves

UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.endPage692es_ES
UDC.grupoInvModelización, Optimización e Inferencia Estatística (MODES)es_ES
UDC.grupoInvModelos e Métodos Numéricos en Enxeñaría e Ciencias Aplicadas (M2NICA)es_ES
UDC.journalTitleMathematical Geoscienceses_ES
UDC.startPage669es_ES
UDC.volume52es_ES
dc.contributor.authorTarrío-Saavedra, Javier
dc.contributor.authorSánchez-Carnero, Noela
dc.contributor.authorPrieto, A.
dc.date.accessioned2025-05-23T11:36:40Z
dc.date.available2025-05-23T11:36:40Z
dc.date.issued2019-05-15
dc.descriptionThis version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any correctionses_ES
dc.description.abstract[Abstract]: Seabed classification in coastal environments is usually accomplished using multivariate methods applied to acoustic features from corrected or uncorrected echoes. This paper presents a comparative study of alternative statistical tools based on time series clustering and non-hierarchical clustering methods for functional data. This allows us to consider the entire acoustic signal without information reduction and assess performance using data acquired in a controlled environment with three different seabed types. The methods considered are used to both analyse the classification power of the recorded echoes and identify the most significant portions of signales_ES
dc.description.sponsorshipThis work has been supported by the Xunta de Galicia through the Centro Singular de Investigación de Galicia ED431G/01, Grupos de Referencia Competitiva ED431C-2016-015, and EM2013/052 projects (Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia), in addition to MINECO Grants MTM2014-52876-R and MTM2017-82724-R, all of them through the ERDF. Also, the authors wish to acknowledge J. A. Rodríguez “Rodri”, skipper of the boat “Betsaida”, from the Ecology and Marine Conservation Research Group, University of Murcia, and Gaston Trobbiani from CESIMAR, for their help with the field workes_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2016-015es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.description.sponsorshipXunta de Galicia; EM2013/052es_ES
dc.identifier.citationTarrío-Saavedra, J., Sánchez-Carnero, N. & Prieto, A. Comparative Study of FDA and Time Series Approaches for Seabed Classification from Acoustic Curves. Math Geosci 52, 669–692 (2020). https://doi.org/10.1007/s11004-019-09807-7es_ES
dc.identifier.issn1874-8953
dc.identifier.issn1874-8961
dc.identifier.urihttp://hdl.handle.net/2183/42073
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRIALESes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSIONes_ES
dc.relation.urihttps://doi.org/10.1007/s11004-019-09807-7es_ES
dc.rights© 2019, International Association for Mathematical Geoscienceses_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectSonares_ES
dc.subjectSupervised classificationes_ES
dc.subjectCluster classificationes_ES
dc.subjectFDAes_ES
dc.subjectTime serieses_ES
dc.titleComparative Study of FDA and Time Series Approaches for Seabed Classification from Acoustic Curveses_ES
dc.typejournal articlees_ES
dc.type.hasVersionAMes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication901462ee-6735-47ee-baf3-4adde32878b9
relation.isAuthorOfPublication33fa4b74-9ac9-4325-9190-3f7c57a50e95
relation.isAuthorOfPublication.latestForDiscovery901462ee-6735-47ee-baf3-4adde32878b9

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