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dc.contributor.advisorGómez Mendoza, Juan Bernardo
dc.contributor.advisorPeluffo Ordóñez, Diego Hernán
dc.creatorMoreno Revelo, Mónica Yolanda
dc.date.accessioned2020-06-23T21:38:41Z
dc.date.available2020-06-23T21:38:41Z
dc.date.created2020
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/77681
dc.descriptionLa clasificación de coberturas a partir del procesamiento de imágenes satelitales puede ser aplicado en diferentes áreas como la clasificación de cultivos y la cuantificación de la deforestación. La clasificación de cultivos es de gran ayuda en temáticas relacionadas con agricultura, ya que la clasificación permite monitorear los cultivos con el fin de mejorar los procesos de producción y disminuir el impacto ambiental relacionado con ella. Por otro lado, la deforestación trae consecuencias negativas como la emisión de más gases invernadero y la desaparición de especies que habitan en bosques. Por lo tanto es importante proponer alternativas que permitan cuantificar la deforestación y regeneración de bosques. Dichas tareas suelen ser llevadas a cabo mediante sistemas computacionales basados en técnicas como el agrupamiento no supervisado y agrupamiento supervisado.\\ La implementación de un sistema de clasificación requiere de ajustes constantes para lograr resultados cada vez más consistentes y oportunos. Por ello trabajar en la clasificación de coberturas en imágenes es aún un problema abierto. Teniendo en cuenta lo anterior, en este trabajo se propone un enfoque basado en procesamiento de imágenes a partir de algunas técnicas de agrupamiento no supervisado y agrupamiento supervisado con etapas de pre-procesamiento y post-procesamiento. Las técnicas fueron validadas sobre diferentes bases de datos, principalmente una base de datos agrícola (Campo Verde), y una base de datos de coberturas boscosas (Nariño). La metodología empleada permitió obtener resultados comparables con los reportados en la literatura.
dc.description.abstractCoverage classification with imagery satellite processing can be applied to topics such as crop classification and deforestation quantification. Crop classification is helpful to issues as agriculture since the classification allows monitoring the crops in order to improve production and lower environmental impact. On the other hand, deforestation has negative consequences such as greenhouse gas emission and species extinction that live in the forests. Therefore, it is important to propose alternatives in order to quantify deforestation and regeneration of trees. Those tasks are often carried out with techniques such as unsupervised grouping and supervised grouping.\\ A classification system requires constant adjustments in order to achieve results more consistent and timely. That is to say, coverage classification is still an open problem. In view of the above, in this work we introduce an approach based on supervised and nonsupervised techniques applied on multispectral satellite imagery using pre-processing and post-processing methods. The techniques were evaluated in different databases, an agricultural database (Campo Verde), and a wooded area database (Nariño). The methodology allowed us to obtain results comparable with state of the art
dc.format.extent95
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.rights.urihttp://creativecommons.org/licenses/by-nd/2.5/co/
dc.subjectdeeep learning
dc.subjectagrupamiento no supervisado
dc.subjectred neuronal convolucional
dc.subjectred neuronal recurrente
dc.subjectimagen satelital
dc.subjectprocesamiento de imágenes - técnicas digitales
dc.subject.ddc000 - Ciencias de la computación, información y obras generales
dc.subject.ddc620 - Ingeniería y operaciones afines::621 - Física aplicada
dc.titleEstudio comparativo de técnicas de visión artificial y procesamiento de imágenes enfocadas a la detección de cambios en coberturas boscosas
dc.title.alternativeComparative study of artificial vision techniques and image processing focused on changes detection in forest covers
dc.typeOther
dc.rights.spaAcceso abierto
dc.contributor.institutionUniversidad Nacional de Colombia - Sede Manizales
dc.subject.keyworddeep learning
dc.subject.keywordsatellite image
dc.subject.keywordunsupervised clustering
dc.subject.keywordconvolutional neural network
dc.subject.keywordrecurrent neural network
dc.subject.keywordImage processing - digital techniques
dc.type.spaOtro
dc.type.hasversionPublished Version
dc.coverage.programDepartamento de Ingeniería Eléctrica y Electrónica
dc.contributor.gruplacComputación Aplicada Suave y Dura (SHAC)
dc.description.additionaltelefóno: 3126305931
dc.coverage.modalityMaestria
dc.rights.accessRightsOpen Access
dc.rights.ccAtribución-SinDerivadas 2.5 Colombia
dc.rights.ccAtribución-SinDerivadas 2.5 Colombia
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dc.contributor.generoFemenino
dc.publisher.programManizales - Ingeniería y Arquitectura - Maestría en Ingeniería - Automatización Industrial


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