Método para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivos

ilustraciones, fotografías, gráficas, tablas

Autores:: Ramírez Gutiérrez, Miguel Angel

Tipo de recurso:

Fecha de publicación:: 2020

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Método para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivos
title	Método para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivos
spellingShingle	Método para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivos 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Imágenes por satélites Tratamiento de imágenes Algoritmos satellite imagery image processing algorithms Orthorectification VHR RFM PSO Cartography OLS Satellite images High resolution Ortorrectificación VHR RFM PSO Cartografía OLS Imágenes satelitales Alta resolución
title_short	Método para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivos
title_full	Método para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivos
title_fullStr	Método para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivos
title_full_unstemmed	Método para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivos
title_sort	Método para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivos
dc.creator.fl_str_mv	Ramírez Gutiérrez, Miguel Angel
dc.contributor.advisor.spa.fl_str_mv	Upegui Cardona, Erika Sofía Leon Sanchez, Camilo Alexander
dc.contributor.author.spa.fl_str_mv	Ramírez Gutiérrez, Miguel Angel
dc.contributor.researchgroup.spa.fl_str_mv	GEFEM: Grupo de Estudio en temas de la Física, de la Estadística y la Matemática
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Imágenes por satélites Tratamiento de imágenes Algoritmos satellite imagery image processing algorithms Orthorectification VHR RFM PSO Cartography OLS Satellite images High resolution Ortorrectificación VHR RFM PSO Cartografía OLS Imágenes satelitales Alta resolución
dc.subject.agrovoc.spa.fl_str_mv	Imágenes por satélites Tratamiento de imágenes Algoritmos
dc.subject.agrovoc.eng.fl_str_mv	satellite imagery image processing algorithms
dc.subject.proposal.eng.fl_str_mv	Orthorectification VHR RFM PSO Cartography OLS Satellite images High resolution
dc.subject.proposal.spa.fl_str_mv	Ortorrectificación VHR RFM PSO Cartografía OLS Imágenes satelitales Alta resolución
description	ilustraciones, fotografías, gráficas, tablas
publishDate	2020
dc.date.issued.spa.fl_str_mv	2020-12-17
dc.date.accessioned.spa.fl_str_mv	2021-01-18T19:54:29Z
dc.date.available.spa.fl_str_mv	2021-01-18T19:54:29Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/78800
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.none.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/78800 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Upegui Cardona, Erika Sofía8a0e54b0af60fb7f6e9a3b5241476f3cLeon Sanchez, Camilo Alexander1e3ecda639909af229f8e6cf11923156Ramírez Gutiérrez, Miguel Angel030848fa-d167-4abe-b5d7-5938940952eaGEFEM: Grupo de Estudio en temas de la Física, de la Estadística y la Matemática2021-01-18T19:54:29Z2021-01-18T19:54:29Z2020-12-17https://repositorio.unal.edu.co/handle/unal/78800Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías, gráficas, tablasLa ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución (VHR, por sus siglas en inglés) espacial es un proceso fundamental para asegurar la interoperabilidad de la información espacial obtenida a partir de ellas y más si se desea generar cartografía básica. Por lo anterior, estudios previos han utilizado distintos tipos de insumos entre los que se destacan múltiples fuentes de puntos de control y modelos digitales de elevación (DEM) de todo tipo, además de probar distintos métodos de optimización. Este trabajo de investigación tiene como objetivo usar de manera conjunta y evaluar la utilización de algoritmo evolutivo Particle Swarm Optimization (PSO, por sus siglas en inglés), puntos estereoscópicos provenientes de bloques fotogramétricos y DEM de distintas fuentes, para la obtención de productos cartográficos de escala 1:10.000 comparando sus resultados con lo obtenido por mínimos cuadrados ordinarios (OLS, por sus siglas en inglés) que ofrece las soluciones comerciales más utilizadas en el mercado. La metodología se compone de tres etapas. La primera corresponde al procedimiento de evaluación de DEM disponibles, generación de bloques fotogramétricos y puntos estereoscópicos junto al aseguramiento de la calidad de estos productos desde un enfoque fotogramétrico. La segunda etapa fue realizada la ortorrectificación de las imágenes monoscópicas VHR utilizando los módulos especializados de las soluciones comerciales (OLS) más utilizadas seleccionando el grado del apropiado del modelo de disposición espacial Rational Functional Model (RFM, por sus siglas en inglés) con su correspondiente evaluación. La tercera y última etapa corresponde a los procesos necesarios para la estimación y selección de los coeficientes del modelo de disposición espacial RFM usando PSO y el método Feature Condition Analysis junto a todo el flujo necesario para la generación de la ortoimagen final junto a una validación de los supuestos estadísticos sobre los residuales. Como resultado de los experimentos con OLS se observa que el uso de los puntos estereoscópicos es adecuado, pero el DEM influencia significativamente la exactitud posicional del producto final, a pesar de no ser adecuados para la escala objetivo. Además, cada algoritmo posee su propio procesamiento traducido en el resultado final y diferente modelo seleccionado, razón de la diferencia en los resultados, por lo que es necesario profundizar con mayor rigor en estos experimentos si se desea estudiar otros tipos de métodos de optimización. Mientras que con el uso del algoritmo PSO se observó mejora en promedio en un 3% la exactitud posicional de la ortoimagen sin embargo su utilización requiere de elevados recursos computacionales y además este tipo de método de optimización no se encuentra disponible aún en software especializado siendo difícil su implementación en masa de procesos productivos cartográficos. (Texto tomado de la fuente).The orthorectification of very high resolution (VHR) monoscopic spatial satellite images is a fundamental process to ensure the interoperability of the spatial information obtained from them. Therefore, previous studies have used different types of inputs, among which multiple sources of control points and digital elevation models (DEM) of all kinds stand out, in addition to testing different optimization methods. This research work aims to jointly use and evaluate the use of the evolutionary algorithm Particle Swarm Optimization (PSO), stereoscopic points from photogrammetric blocks and DEM from different sources, to obtain cartographic products of scale 1:10.000 comparing its results with that obtained by Ordinary Least Squares (OLS) that offers the most used commercial solutions. The methodology is made up of three stages. The first stage corresponds to the available DEM evaluation procedure, generation of photogrammetric blocks and stereoscopic points, together with the quality assurance of these products from a photogrammetric approach. The second stage was performed the orthorectification of the monoscopic VHR images using the specialized modules of the most used commercial solutions (OLS), selecting the degree of the appropriate spatial arrangement model Rational Functional Model (RFM) with its corresponding evaluation. The third and last stage corresponds to the processes necessary for the estimation and selection of the coefficients of the RFM spatial arrangement model using PSO and the Feature Condition Analysis method together with all the necessary flow for the generation of the final orthoimage together to a validation of the statistical assumptions about the residuals. As a result of the OLS experiments, it is observed that the use of stereoscopic points is adequate, but the DEM significantly influences the positional accuracy of the final product, despite not being suitable for the target scale. In addition, each algorithm has its own processing translated into the final result and a different selected model, which is the reason for the difference in the results, so it is necessary to delve more rigorously into these experiments if you want to study other types of optimization methods. While with the use of the PSO algorithm, an average 3 \% improvement in the positional accuracy of the orthoimage was observed; however, its use requires high computational resources and, furthermore, this type of optimization method is not yet available in specialized software. difficult its mass implementation of cartographic production processes.Incluye anexosMaestríaMagíster en GeomáticaTecnologías geoespacialesCiencias Agronómicasxix, 82 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaEscuela de posgradosFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresImágenes por satélitesTratamiento de imágenesAlgoritmossatellite imageryimage processingalgorithmsOrthorectificationVHRRFMPSOCartographyOLSSatellite imagesHigh resolutionOrtorrectificaciónVHRRFMPSOCartografíaOLSImágenes satelitalesAlta resoluciónMétodo para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivosTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[Aber et al., 2019] Aber, J. 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Método para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivos

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