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Characterization of seaweed communities using deep learning applied to UAV-based hyperspectral images

2021Conference paper Open access
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dc.contributor.authorGomes, João Pedro
dc.contributor.authorSousa, Joaquim J.
dc.contributor.authorPádua, Luís
dc.contributor.authorCunha, Carlos R.
dc.contributor.authorCunha, António
dc.date.accessioned2023-02-28T14:40:09Z
dc.date.available2023-02-28T14:40:09Z
dc.date.issued2021
dc.description.abstractMacroalgal communities are generally found in coastal regions, close to rocks or other hard surfaces. They provide shelter and food for many organisms and are of interest to the food industry, pharmaceutical, and agriculture. They are also an indicator of environmental change. Traditionally, the process of identification and monitoring of these communities and their constituent species is based on manual methods. The use of unmanned aerial vehicles (UAVs) allows the remote collection of images with high spatial and spectral resolution and adjustable time scales. The development of methodologies allowing the processing and the analysis of UAV-based high-resolution imagery would be of economic and environmental importance. That would allow to streamline the identification of species with economic potential, the evaluation of the seasonal and spatial variation of the available biomass, and the monitoring of the coastal ecological status and its evolution. Recent technological developments in the areas of remote sensing and artificial intelligence make it possible to provide tools with great potential for these applications. Indeed, hyperspectral sensors can nowadays be coupled in UAVs allowing for high spatial and spectral resolution imagery. The data processing powered by deep learning and its increasing diversity of models and architectures is the ideal way to handle and analyze the huge volume of data acquired. In this paper, we describe a methodology to be implemented in a system to be developed to make the automatic classification of existing species in macroalgal communities, using deep learning models applied to hyperspectral images collected by UAVs.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.citationGomes, João Pedro; Sousa, Joaquim J.; Pádua, Luís; Cunha, Carlos R.; Cunha, António (2021). Characterization of seaweed communities using deep learning applied to UAV-based hyperspectral images. In Conference on ENTERprise Information Systems / ProjMAN 2021 - International Conference on Project MANagement / HCist 2021 - International Conference on Health and Social Care Information Systems and Technologies. p. 1-3pt_PT
dc.identifier.urihttp://hdl.handle.net/10198/27306
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSeaweedpt_PT
dc.subjectMacroalgaept_PT
dc.subjectHyperspectralpt_PT
dc.subjectUAVpt_PT
dc.subjectDeep learningpt_PT
dc.subjectMachine Learningpt_PT
dc.titleCharacterization of seaweed communities using deep learning applied to UAV-based hyperspectral imagespt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage3pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleCENTERIS 2021 - Conference on Enterprise Information Systems / ProjMAN 2021 - International Conference on Project MANagement / HCist 2021 - International Conference on Health and Social Care Information Systems and Technologiespt_PT
person.familyNameGomes
person.familyNameCunha
person.givenNameJoão Pedro
person.givenNameCarlos R.
person.identifierR-001-NSC
person.identifier.ciencia-id4A1D-E1E3-3F27
person.identifier.ciencia-id2316-5664-FF6F
person.identifier.orcid0000-0001-9308-0027
person.identifier.orcid0000-0003-3085-1562
person.identifier.ridH-2678-2014
person.identifier.scopus-author-id57202512811
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication8740f6a8-8951-46cd-9719-74edc43b1c74
relation.isAuthorOfPublication14626e32-5646-48ad-9242-30944450dd8e
relation.isAuthorOfPublication.latestForDiscovery8740f6a8-8951-46cd-9719-74edc43b1c74

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