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dc.contributorEscuela de Ingeniería Agraria y Forestales_ES
dc.contributor.authorBalado, Jesús
dc.contributor.authorRodríguez Pérez, José Ramón 
dc.contributor.authorArias, Pedro
dc.contributor.authorOlabarria, Celia
dc.contributor.authorMartinez Sánchez, Joaquín
dc.contributor.otherIngeniería Cartografica, Geodesica y Fotogrametriaes_ES
dc.date2021-01-01
dc.date.accessioned2024-01-30T10:58:26Z
dc.date.available2024-01-30T10:58:26Z
dc.identifier.citationJesús Balado, Celia Olabarria, Joaquín Martínez-Sánchez, José R. Rodríguez-Pérez & Arias Pedro (2021) Semantic segmentation of major macroalgae in coastal environments using high-resolution ground imagery and deep learning, International Journal of Remote Sensing, 42:5, 1785-1800 is available at https://doi.org/10.1080/01431161.2020.1842543es_ES
dc.identifier.issn1366-5901
dc.identifier.urihttps://hdl.handle.net/10612/17918
dc.description.abstractMacroalgae are a fundamental component of coastal ecosystems and play a key role in shaping community structure and functioning. Macroalgae are currently threatened by diverse stressors, particularly climate change and invasive species, but they do not all respond in the same way to the stressors. Effective methods of collecting qualitative and quantitative information are essential to enable better, more efficient management of macroalgae. Acquisition of high-resolution images, in which macroalgae can be distinguished on the basis of their texture and colour, and the automated processing of these images are thus essential. Although ground images are useful, labelling is tedious. This study focuses on the semantic segmentation of five macroalgal species in high-resolution ground images taken in 0.5 x 0.5 m quadrats placed along an intertidal rocky shore at low tide. The target species, Bifurcaria bifurcata, Cystoseira tamariscifolia, Sargassum muticum, Sacchoriza polyschides and Codium spp., which predominate on intertidal shores, belong to different morpho-functional groups. The study explains how to convert vector-labelled data to raster-labelled data for adaptation to convolutional neural network (CNN) input. Three CNNs (MobileNetV2, Resnet18, Xception) were compared, and ResNet18 yielded the highest accuracy (91.9%). The macroalgae were correctly segmented, and the main confusion occurred at the borders between different macroalgal species, a problem derived from labelling errors. In addition, the interior and exterior of the quadrats were correctly delimited by the CNNs. The results were obtained from only one hundred labelled images and can be performed on personal computers, without the need to resort to external servers. The proposed method helps automation of the labelling process.es_ES
dc.languageenges_ES
dc.publisherTaylor & Francises_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectEcología. Medio ambientees_ES
dc.subject.otherMacroalgaees_ES
dc.subject.otherIntertidal rocky shorees_ES
dc.subject.otherConvolutional neural networkses_ES
dc.subject.otherImage processinges_ES
dc.subject.otherSemantic segmentationes_ES
dc.titleSemantic segmentation of major macroalgae in coastal environments using high-resolution ground imagery and deep learninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doihttps://doi.org/10.1080/01431161.2020.1842543
dc.description.peerreviewedSIes_ES
dc.relation.projectIDED481B-2019-061es_ES
dc.relation.projectIDED431C 2016-038es_ES
dc.relation.projectIDRTI2018-095893-B-C21es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.journal.titleInternational Journal of Remote Sensinges_ES
dc.volume.number42es_ES
dc.issue.number5es_ES
dc.page.initial1785es_ES
dc.page.final1800es_ES
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES
dc.description.projectFundación Biodiversidad, the Ministerio para la Transición Ecológica y 383 el Reto Demográfico through the Pleamar program, co-funded by the European Maritime and Fisheries Fund (EMFF), call 2018; and Xunta de Galicia for human resources and competitive reference groupses_ES
dc.description.projectFundación Biodiversidad, the Ministerio para la Transición Ecológica y 383 el Reto Demográfico through the Pleamar program, co-funded by the European Maritime and Fisheries Fund (EMFF), call 2018; and Xunta de Galicia for human resources and competitive reference groupses_ES
dc.description.projectMinisterio de Ciencia, Innovación y Universidades -Gobierno de Españaes_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional