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

dc.contributor.advisorUpegui Cardona, Erika Sofíaspa
dc.contributor.advisorLeon Sanchez, Camilo Alexanderspa
dc.contributor.authorRamírez Gutiérrez, Miguel Angelspa
dc.contributor.researchgroupGEFEM: Grupo de Estudio en temas de la Física, de la Estadística y la Matemáticaspa
dc.date.accessioned2021-01-18T19:54:29Zspa
dc.date.available2021-01-18T19:54:29Zspa
dc.date.issued2020-12-17spa
dc.descriptionilustraciones, fotografías, gráficas, tablasspa
dc.description.abstractLa 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).spa
dc.description.abstractThe 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.eng
dc.description.curricularareaCiencias Agronómicasspa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Geomáticaspa
dc.description.notesIncluye anexosspa
dc.description.researchareaTecnologías geoespacialesspa
dc.format.extentxix, 82 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/78800
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentEscuela de posgradosspa
dc.publisher.facultyFacultad de Ciencias Agrariasspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ciencias Agrarias - Maestría en Geomáticaspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.agrovocImágenes por satélitesspa
dc.subject.agrovocsatellite imageryeng
dc.subject.agrovocTratamiento de imágenesspa
dc.subject.agrovocimage processingeng
dc.subject.agrovocAlgoritmosspa
dc.subject.agrovocalgorithmseng
dc.subject.ddc000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresspa
dc.subject.proposalOrthorectificationeng
dc.subject.proposalVHReng
dc.subject.proposalRFMeng
dc.subject.proposalPSOeng
dc.subject.proposalCartographyeng
dc.subject.proposalOLSeng
dc.subject.proposalSatellite imageseng
dc.subject.proposalHigh resolutioneng
dc.subject.proposalOrtorrectificaciónspa
dc.subject.proposalVHRspa
dc.subject.proposalRFMspa
dc.subject.proposalPSOspa
dc.subject.proposalCartografíaspa
dc.subject.proposalOLSspa
dc.subject.proposalImágenes satelitalesspa
dc.subject.proposalAlta resoluciónspa
dc.titleMétodo para la ortorrectificación de imágenes satelitales monoscópicas de muy alta resolución espacial empleando algoritmos evolutivosspa
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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