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Clasificación y mapeo automático de coberturas del suelo en imágenes satelitales utilizando Redes Neuronales Convolucionales

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dc.contributor.authorSuárez Londoño, Arnol Sneiderspa
dc.contributor.authorJiménez López, Andrés Fernandospa
dc.contributor.authorCastro Franco, Mauriciospa
dc.contributor.authorCruz Roa, Angel Alfonsospa
dc.date.accessioned2017-07-16 00:00:00
dc.date.accessioned2022-06-13T17:42:09Z
dc.date.available2017-07-16 00:00:00
dc.date.available2022-06-13T17:42:09Z
dc.date.issued2017-07-16
dc.description.abstractLa clasificación de cobertura del suelo es importante para estudios de cambio climático y monitoreo de servicios ecosistémicos. Los métodos convencionales de clasificación de coberturas se realizan mediante la interpretación visual de imágenes satelitales, lo cual es costoso, dispendioso e impreciso. Implementar métodos computacionales permite generar clasificación de coberturas en imágenes satelitales de manera automática, rápida, precisa y económica. Particularmente, los métodos de aprendizaje automático son técnicas computacionales promisorias para la estimación de cambios de cobertura del suelo. En este trabajo se presenta un método de aprendizaje automático basado en redes neuronales convolucionales de arquitectura tipo ConvNet para la clasificación automática de coberturas del suelo a partir de imágenes Landsat 5 TM. La ConvNet fue entrenada a partir de las anotaciones manuales por medio de interpretación visual sobre las imágenes satelitales con las que los expertos generaron el mapa de cobertura del parque nacional el Tuparro, de los Parques Nacionales Naturales de Colombia. El modelo de validación se realizó con datos de los mapas de coberturas del Amazonas colombiano realizado por el Sistema de Información Ambiental de Colombia. Los resultados obtenidos de la diagonal de la matriz de confusión de la exactitud promedio fue de 83.27% en entrenamiento y 91.02% en validación; para la clasificación en parches entre Bosques, áreas con vegetación herbácea y/o arbustiva, áreas abiertas sin o con poca vegetación y aguas continentales.spa
dc.description.abstractLand cover classification is important for studies of climate change and monitoring of ecosystem services. Conventional coverage classification methods are performed by the visual interpretation of satellite imagery, which is expensive and inaccurate. Implementing computational methods could generate procedures to classify coverage in satellite images automatically, quickly, accurately and economically. Particularly, automatic learning methods are promising computational methods for estimating soil cover changes. In this work we present an automatic learning method based on convolutional neural networks of ConvNet type architecture for the automatic classification of soil coverings from Landsat 5 TM images. The ConvNet was trained from the manual annotations by means of visual interpretation on the satellite images with which the experts generated the map of Tuparro national park, of National Natural Park of Colombia. The validation model was performed with data from the Colombian Amazon cover maps made by the Colombian Environmental Information System. The results obtained from the diagonal of the confusion matrix of the average accuracy were 83.27% in training and 91.02% in validation; for the classification in patches between forests, areas with herbaceous and / or shrub vegetation, open areas with or without vegetation and Inland waters.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doi10.22579/20112629.432
dc.identifier.eissn2011-2629
dc.identifier.issn0121-3709
dc.identifier.urihttps://repositorio.unillanos.edu.co/handle/001/2654
dc.identifier.urlhttps://doi.org/10.22579/20112629.432
dc.language.isospaspa
dc.publisherUniversidad de los Llanosspa
dc.relation.bitstreamhttps://orinoquia.unillanos.edu.co/index.php/orinoquia/article/download/432/1023
dc.relation.citationeditionNúm. 1 Sup , Año 2017spa
dc.relation.citationendpage75
dc.relation.citationissue1 Supspa
dc.relation.citationstartpage64
dc.relation.citationvolume21spa
dc.relation.ispartofjournalOrinoquiaspa
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dc.rightsOrinoquia - 2019spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/spa
dc.sourcehttps://orinoquia.unillanos.edu.co/index.php/orinoquia/article/view/432spa
dc.subjectEditorialeng
dc.subjectEditorialspa
dc.titleClasificación y mapeo automático de coberturas del suelo en imágenes satelitales utilizando Redes Neuronales Convolucionalesspa
dc.title.translatedClassification and automatic mapping of land covers in satellite images using Convolutional Neural Networkseng
dc.typeArtículo de revistaspa
dc.typeJournal Articleeng
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.localSección Artículosspa
dc.type.localSección Articleseng
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
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