Método para extracción de coberturas urbanas basado en análisis orientado a objetos geográficos a partir de imágenes de alta resolución y modelos digitales de superficie
imágenes, ilustraciones, gráficas, tablas
- Autores:
-
Mora Castañeda, Deybi Libardo
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/80302
- Palabra clave:
- 620 - Ingeniería y operaciones afines
GEOBIA
Random Forest
Multiresolution Segmentation
Clasificación de coberturas urbanas
Imágenes de alta resolución
Optimización
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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UNACIONAL2_1d6ad725b0aa5be2938689e3b4407cb2 |
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| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/80302 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
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|
| dc.title.spa.fl_str_mv |
Método para extracción de coberturas urbanas basado en análisis orientado a objetos geográficos a partir de imágenes de alta resolución y modelos digitales de superficie |
| dc.title.translated.eng.fl_str_mv |
Urban land-cover extraction method based geographic-object analysis from very-high resolution imagery and digital surface models |
| title |
Método para extracción de coberturas urbanas basado en análisis orientado a objetos geográficos a partir de imágenes de alta resolución y modelos digitales de superficie |
| spellingShingle |
Método para extracción de coberturas urbanas basado en análisis orientado a objetos geográficos a partir de imágenes de alta resolución y modelos digitales de superficie 620 - Ingeniería y operaciones afines GEOBIA Random Forest Multiresolution Segmentation Clasificación de coberturas urbanas Imágenes de alta resolución Optimización |
| title_short |
Método para extracción de coberturas urbanas basado en análisis orientado a objetos geográficos a partir de imágenes de alta resolución y modelos digitales de superficie |
| title_full |
Método para extracción de coberturas urbanas basado en análisis orientado a objetos geográficos a partir de imágenes de alta resolución y modelos digitales de superficie |
| title_fullStr |
Método para extracción de coberturas urbanas basado en análisis orientado a objetos geográficos a partir de imágenes de alta resolución y modelos digitales de superficie |
| title_full_unstemmed |
Método para extracción de coberturas urbanas basado en análisis orientado a objetos geográficos a partir de imágenes de alta resolución y modelos digitales de superficie |
| title_sort |
Método para extracción de coberturas urbanas basado en análisis orientado a objetos geográficos a partir de imágenes de alta resolución y modelos digitales de superficie |
| dc.creator.fl_str_mv |
Mora Castañeda, Deybi Libardo |
| dc.contributor.advisor.none.fl_str_mv |
Lizarazo Salcedo, Iván Alberto León Sánchez, Camilo Alexander |
| dc.contributor.author.none.fl_str_mv |
Mora Castañeda, Deybi Libardo |
| dc.contributor.researchgroup.spa.fl_str_mv |
Análisis Espacial del Territorio y del Cambio Global (AET-CG) |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines |
| topic |
620 - Ingeniería y operaciones afines GEOBIA Random Forest Multiresolution Segmentation Clasificación de coberturas urbanas Imágenes de alta resolución Optimización |
| dc.subject.proposal.eng.fl_str_mv |
GEOBIA Random Forest Multiresolution Segmentation |
| dc.subject.proposal.spa.fl_str_mv |
Clasificación de coberturas urbanas Imágenes de alta resolución Optimización |
| description |
imágenes, ilustraciones, gráficas, tablas |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2021-09-24T21:53:17Z |
| dc.date.available.none.fl_str_mv |
2021-09-24T21:53:17Z |
| 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 |
DataPaper |
| 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/80302 |
| 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.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/80302 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 |
| dc.relation.references.spa.fl_str_mv |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lizarazo Salcedo, Iván Albertob7a911d83f30c19f50a8d2f7b4e94e02600León Sánchez, Camilo Alexander5babbcf04c4bb0d0a22aeca66ef8b56cMora Castañeda, Deybi Libardo2fef4a848906549e8e15583460b33c62600Análisis Espacial del Territorio y del Cambio Global (AET-CG)2021-09-24T21:53:17Z2021-09-24T21:53:17Z2020https://repositorio.unal.edu.co/handle/unal/80302Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/imágenes, ilustraciones, gráficas, tablasLa clasificación de coberturas urbanas a partir de datos de sensores remotos aerotransportados de alta resolución espacial ha sido un tema de gran interés para investigadores; instituciones y gobiernos. Sin embargo, debido a la heterogeneidad espacial y a la similitud espectral de las clases de coberturas a clasificar, es una tarea que no ha sido completamente resuelta. Por tal razón, esta investigación propuso el desarrollo de un método de clasificación orientado a objetos geográficos, que evaluó y escogió las variables e hiperparámetros que mejor resultado de exactitud generaron. Para llevar a cabo este propósito, se establecieron cuatro fases descritas de la siguiente manera: (1) Selección de variables de entrada para la segmentación, (2) Segmentación y optimización de hiperparámetros del algoritmo de Segmentación por Multirresolución, (3) Selección de atributos de objetos geográficos y (4) Clasificación y optimización de hiperparámetros del algoritmo de aprendizaje automático Bosques Aleatorios. Este método se implementó en una zona de estudio de arquitectura urbana residencial y antigua, ubicada en la ciudad alemana de Vaihingen an der Enz, al noroccidente de Stuttgart. Se utilizaron imágenes ortorrectificadas de alta resolución y modelos digitales de superficie del Semantic Challenge de la Sociedad Internacional de Percepción Remota y Fotogrametría (ISPRS). La investigación permitió comprobar la eficiencia de variables derivadas de los datos primarios, como el índice de vegetación de diferencia normalizada o el modelo digital de superficie normalizado, en las fases de segmentación y clasificación. También demostró que la optimización de hiperparámetros en la segmentación y clasificación permitió obtener mejoras tanto en exactitud como en rendimiento. En conclusión, el método desarrollado evidenció que, optimizar las fases de selección de variables; segmentación; selección de atributos y clasificación de un método de análisis de imágenes orientado a objetos geográficos ayuda a mejorar la clasificación de coberturas urbanas respecto a un método no optimizado. (Texto tomado de la fuente)The urban land-cover classification based on airborne remote sensing data with the high spatial resolution has been a topic of great interest to researchers, institutions, and governments. However, due to the spatial heterogeneity and the spectral similarity of the coverage classes to be classified, the issue has not been completely solved. Accordingly, this research proposed the development of a geographic object-based classification method, which evaluated and selected the variables and hyperparameters that better fit accuracy results. For this purpose, the following four stages were established: (1) Selection of segmentation input variables, (2) Segmentation and optimization of Multiresolution Segmentation algorithm hyperparameters, (3) Selection of geographical objects features, and (4) Classification and optimization of hyperparameters for the machine learning Random Forests algorithm. The study area where the method was implemented has urban residential and antique architecture, it is located in the German city of Vaihingen an der Enz, northwest of Stuttgart. High-resolution orthorectified images and digital surface models from the International Society for Photogrammetry and Remote Sensing (ISPRS) Semantic Challenge were used. This research checked the efficiency of variables derived from primary data, such as the normalized difference vegetation index or the normalized digital surface model, in the segmentation and classification phases. Moreover, the research project showed that optimizing hyperparameters in segmentation and classification allowed for improvements in both accuracy and performance. In conclusion, the developed method showed that optimizing the variable selection; segmentation; feature selection, and classification phases of geographic object-based image analysis method helps improve the classification of urban land-cover with respect to a non-optimized method.MaestríaMagíster en GeomáticaTecnologías Geoespacialesxxi, 1328 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaEscuela de posgradosFacultad de Ciencias AgrariasUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afinesGEOBIARandom ForestMultiresolution SegmentationClasificación de coberturas urbanasImágenes de alta resoluciónOptimizaciónMétodo para extracción de coberturas urbanas basado en análisis orientado a objetos geográficos a partir de imágenes de alta resolución y modelos digitales de superficieUrban land-cover extraction method based geographic-object analysis from very-high resolution imagery and digital surface modelsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionDataPaperhttp://purl.org/redcol/resource_type/TMAguilar, M. 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Object based image analysis combining high spatial resolution imagery and laser point clouds for urban land cover. 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