Exploración de características geológicas de la superficie lunar de Encélado (Saturno) utilizando técnicas de aprendizaje automático para la clasificación de imágenes satelitales

ilustraciones, diagramas, fotografías, tablas

Autores:
Forero Larrotta, Juliana Paola
Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/86800
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/86800
https://repositorio.unal.edu.co/
Palabra clave:
310 - Colecciones de estadística general
520 - Astronomía y ciencias afines
000 - Ciencias de la computación, información y obras generales
GEOLOGÍA LUNAR
SONDAS ESPACIALES
CRONOLOGÍA GEOLÓGICA
METEORITOS
CRATERES METEORICOS
PROPIEDADES FISICOQUÍMICAS
Lunar geology
Space probes
Geological time
Meteorites
Meteorite craters
Chemicophysical properties
Aprendizaje automático
Aprendizaje profundo
Redes neuronales
Transferencia de aprendizaje
Imágenes satelitales
Aprendizaje no supervisado
Clustering
Machine learning
Deep learning
Neural networks
Unsupervised learning
Satellite images
Transfer learning
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional
id UNACIONAL2_6c5137a3514392eb7bdb5ab24f0ab912
oai_identifier_str oai:repositorio.unal.edu.co:unal/86800
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Exploración de características geológicas de la superficie lunar de Encélado (Saturno) utilizando técnicas de aprendizaje automático para la clasificación de imágenes satelitales
dc.title.translated.eng.fl_str_mv Exploration of geological features of the lunar surface of Enceladus (Saturn) using machine learning and deep learning techniques for image classification
title Exploración de características geológicas de la superficie lunar de Encélado (Saturno) utilizando técnicas de aprendizaje automático para la clasificación de imágenes satelitales
spellingShingle Exploración de características geológicas de la superficie lunar de Encélado (Saturno) utilizando técnicas de aprendizaje automático para la clasificación de imágenes satelitales
310 - Colecciones de estadística general
520 - Astronomía y ciencias afines
000 - Ciencias de la computación, información y obras generales
GEOLOGÍA LUNAR
SONDAS ESPACIALES
CRONOLOGÍA GEOLÓGICA
METEORITOS
CRATERES METEORICOS
PROPIEDADES FISICOQUÍMICAS
Lunar geology
Space probes
Geological time
Meteorites
Meteorite craters
Chemicophysical properties
Aprendizaje automático
Aprendizaje profundo
Redes neuronales
Transferencia de aprendizaje
Imágenes satelitales
Aprendizaje no supervisado
Clustering
Machine learning
Deep learning
Neural networks
Unsupervised learning
Satellite images
Transfer learning
title_short Exploración de características geológicas de la superficie lunar de Encélado (Saturno) utilizando técnicas de aprendizaje automático para la clasificación de imágenes satelitales
title_full Exploración de características geológicas de la superficie lunar de Encélado (Saturno) utilizando técnicas de aprendizaje automático para la clasificación de imágenes satelitales
title_fullStr Exploración de características geológicas de la superficie lunar de Encélado (Saturno) utilizando técnicas de aprendizaje automático para la clasificación de imágenes satelitales
title_full_unstemmed Exploración de características geológicas de la superficie lunar de Encélado (Saturno) utilizando técnicas de aprendizaje automático para la clasificación de imágenes satelitales
title_sort Exploración de características geológicas de la superficie lunar de Encélado (Saturno) utilizando técnicas de aprendizaje automático para la clasificación de imágenes satelitales
dc.creator.fl_str_mv Forero Larrotta, Juliana Paola
dc.contributor.advisor.spa.fl_str_mv Montenegro Diaz, Alvaro Mauricio
dc.contributor.author.spa.fl_str_mv Forero Larrotta, Juliana Paola
dc.subject.ddc.spa.fl_str_mv 310 - Colecciones de estadística general
520 - Astronomía y ciencias afines
000 - Ciencias de la computación, información y obras generales
topic 310 - Colecciones de estadística general
520 - Astronomía y ciencias afines
000 - Ciencias de la computación, información y obras generales
GEOLOGÍA LUNAR
SONDAS ESPACIALES
CRONOLOGÍA GEOLÓGICA
METEORITOS
CRATERES METEORICOS
PROPIEDADES FISICOQUÍMICAS
Lunar geology
Space probes
Geological time
Meteorites
Meteorite craters
Chemicophysical properties
Aprendizaje automático
Aprendizaje profundo
Redes neuronales
Transferencia de aprendizaje
Imágenes satelitales
Aprendizaje no supervisado
Clustering
Machine learning
Deep learning
Neural networks
Unsupervised learning
Satellite images
Transfer learning
dc.subject.lemb.spa.fl_str_mv GEOLOGÍA LUNAR
SONDAS ESPACIALES
CRONOLOGÍA GEOLÓGICA
METEORITOS
CRATERES METEORICOS
PROPIEDADES FISICOQUÍMICAS
dc.subject.lemb.eng.fl_str_mv Lunar geology
Space probes
Geological time
Meteorites
Meteorite craters
Chemicophysical properties
dc.subject.proposal.spa.fl_str_mv Aprendizaje automático
Aprendizaje profundo
Redes neuronales
Transferencia de aprendizaje
Imágenes satelitales
Aprendizaje no supervisado
dc.subject.proposal.eng.fl_str_mv Clustering
Machine learning
Deep learning
Neural networks
Unsupervised learning
Satellite images
Transfer learning
description ilustraciones, diagramas, fotografías, tablas
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-09-06T15:01:46Z
dc.date.available.none.fl_str_mv 2024-09-06T15:01:46Z
dc.date.issued.none.fl_str_mv 2024
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/86800
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/86800
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_abf2Montenegro Diaz, Alvaro Mauricio1b2d2a662f277390ca72c69edcb417dfForero Larrotta, Juliana Paola07990fa1699fc338f004cb4c6a4ca9192024-09-06T15:01:46Z2024-09-06T15:01:46Z2024https://repositorio.unal.edu.co/handle/unal/86800Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, fotografías, tablasEl análisis de imágenes satelitales brinda mucha información valiosa que puede ser aplicada en diferentes contextos. En el caso de los cuerpos planetarios, el análisis de imágenes tomadas por sondas espaciales es útil para determinar el origen, evolución, distribución y comportamiento geológico de un cuerpo del sistema solar (planetas, lunas, meteoritos). Gracias a estos datos podemos determinar la edad geológica relativa de un cuerpo con base en impactos de meteoritos observados que como consecuencia dejan cráteres y deforman superficies planetarias y lunares, incluso podemos determinar distintas propiedades fisicoquímicas que nos pueden dar indicio de la existencia de fuentes de agua, composiciones atmosféricas, abundancia de elementos y minerales de interés y así comprender mejor la mecánica interna y externa del cuerpo. Este tipo de aplicación requiere la identificación manual de particularidades y características morfológicas y composicionales en cientos de imágenes tomadas por diferentes instrumentos en diferentes longitudes de onda, con diferentes características de resolución, ángulo de captura de la imagen, tiempo de exposición, longitud de onda captada, posición del cuerpo planetario respecto a su estrella más cercana, ente otros factores. El presente trabajo es una aplicación de modelos de aprendizaje automático de tipo no supervisado como el clustering para el procesamiento de imágenes de la luna de Encelado del planeta Saturno tomadas por la sonda Cassini-Huygens entre los años 2005 y 2017 y que se pueden encontrar en el siguiente repositorio del proyecto PILOT de la NASA (Planetary Image Locator Tool) (USGS/NASA, 2015, https://pilot.wr.usgs.gov), el cual es el archivo más completo de imágenes tomadas por sondas enviadas al espacio hasta la fecha. La clasificación de las imágenes tomadas por la sonda Cassini-Huygens permite ampliar la comprensión de los diferentes procesos que dieron lugar a una gran variedad de características morfológicas y tectónicas de su superficie, ya que basta con observar y clasificar distintos tipos de geoformas como lo son cráteres de impacto, fracturas, fallas, surcos, elevaciones, montañas, distribución y tamaño de partículas, para entender la dinámica geológica de la luna y su dinámica criovolcánica. Se plantea un marco de trabajo para la aplicación de modelos de aprendizaje automático no supervisado como el k-means, MeanShift, DBSCAN y Mixtura Gaussiana para abordar el problema de segmentación de la imagen y detección de particularidades en la clasificación de morfologías, ya que este tipo de algoritmos permite dividir un conjunto de imágenes en grupos basados en sus características o propiedades identificadas, adicionalmente se entrena un modelo de red neuronal convolucional que toma las imágenes etiquetadas con k-means y busca predecir la clase sobre nuevas imágenes. Se prueban distintas combinaciones de técnicas de preprocesamiento y extracción de características y se aplica la técnica de transferencia de aprendizaje en modelos de redes neuronales preentrenadas tanto para poder extraer las características de una imagen, como para poder entrenar un clasificador que permita agrupar nuevas imágenes lunares en las categorías identificadas. Para Encélado, la sonda Cassini Huygens cuenta con dos tipos de instrumentos para la toma de datos: ISS (Cassini Imaging Science Subsystem) y VIMS (Visual and Infrared Mapping Spectrometer), los cuales producen imágenes de alta resolución. Se usaron 5167 imágenes mapeadas mediante un lente NA (Narrow Angle), es decir, un ángulo de imagen normal y no más amplio, del instrumento ISS que cuenta con imágenes tanto en el canal visible como en el infrarrojo cercano, estas imágenes fueron tomadas a distintas distancias y capturan distintas regiones de la luna (Texto tomado de la fuente).The analysis of satellite images provides valuable information that can be applied in different contexts. In the case of planetary bodies, the analysis of images taken by space probes is useful to determine the origin, evolution, distribution and geological behavior of a solar system body (planets, moons, meteorites). Thanks to these data we can determine the relative geological age of a body based on observed meteorite impacts that as a consequence leave craters and deform planetary and lunar surfaces, we can even determine different physicochemical properties that can give us an indication of the existence of water sources, atmospheric compositions, abundance of elements and minerals of interest and thus better understand the internal and external mechanics of the body. This type of application requires the manual identification of morphological and compositional features and characteristics in hundreds of images taken by different instruments at different wavelengths, with different resolution characteristics, image capture angle, exposure time, wavelength captured, position of the planetary body respect to its nearest star, among other factors. The present work is an application of unsupervised machine learning models such as clustering for the processing of images of the Enceladus moon of the planet Saturn taken by the Cassini-Huygens probe between the years 2005 and 2017 and that can be found in the following repository of NASA’s PILOT (Planetary Image Locator Tool) project (USGS/NASA, 2015, https://pilot.wr.usgs.gov), which is the most complete archive of images taken by probes sent to space to date. The classification of the images taken by the Cassini-Huygens probe allows to understand the different processes that gave rise to a great variety of morphological and tectonic characteristics of its surface, since it is enough to observe and classify different types of geoforms such as impact craters, fractures, faults, grooves, elevations, mountains, distribution and size of particles, to understand the geological dynamics of the moon and its cryovolcanic dynamics. A framework is proposed for the application of unsupervised machine learning models such as k-means, MeanShift, DBSCAN and Gaussian Mixture to address the problem of image segmentation and detection of particularities in the classification of morphologies, since this type of algorithms allows dividing a set of images into groups or clusters based on their identified characteristics or properties. In addition, a convolutional neural network model is trained that takes the images labeled with k-means and seeks to predict the class on new images. Different combinations of preprocessing and feature extraction techniques are tested and the transfer learning technique is applied to pre-trained neural network models both to extract features from an image and to train a classifier to group new lunar images into the identified categories. For Enceladus, the Cassini Huygens probe has two types of instruments for data acquisition: ISS (Cassini Imaging Science Subsystem) and VIMS (Visual and Infrared Mapping Spectrometer), which produce high-resolution images. We used 5167 images mapped by means of a NA (Narrow Angle) lens, that is, a normal image angle and not wider, of the ISS instrument that has images in both the visible and near infrared channels, these images were taken at different distances and capture different regions of the moon.MaestríaMagíster en Ciencias - Estadísticaviii, 162 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá310 - Colecciones de estadística general520 - Astronomía y ciencias afines000 - Ciencias de la computación, información y obras generalesGEOLOGÍA LUNARSONDAS ESPACIALESCRONOLOGÍA GEOLÓGICAMETEORITOSCRATERES METEORICOSPROPIEDADES FISICOQUÍMICASLunar geologySpace probesGeological timeMeteoritesMeteorite cratersChemicophysical propertiesAprendizaje automáticoAprendizaje profundoRedes neuronalesTransferencia de aprendizajeImágenes satelitalesAprendizaje no supervisadoClusteringMachine learningDeep learningNeural networksUnsupervised learningSatellite imagesTransfer learningExploración de características geológicas de la superficie lunar de Encélado (Saturno) utilizando técnicas de aprendizaje automático para la clasificación de imágenes satelitalesExploration of geological features of the lunar surface of Enceladus (Saturn) using machine learning and deep learning techniques for image classificationTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMSmoothing Images. 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In: OpenCV Consulting Site (2024)EstudiantesInvestigadoresMaestrosMedios de comunicaciónPadres y familiasPersonal de apoyo escolarProveedores de ayuda financiera para estudiantesPúblico generalORIGINAL1026277472.2024.pdf1026277472.2024.pdfTesis de Maestría en Ciencias Estadísticaapplication/pdf25470025https://repositorio.unal.edu.co/bitstream/unal/86800/4/1026277472.2024.pdfa37425a403882802dc34ed0b2d6974bdMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86800/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53THUMBNAIL1026277472.2024.pdf.jpg1026277472.2024.pdf.jpgGenerated Thumbnailimage/jpeg5005https://repositorio.unal.edu.co/bitstream/unal/86800/5/1026277472.2024.pdf.jpg3285a893e9e1ac6c74cbbfc3e1ef8e80MD55unal/86800oai:repositorio.unal.edu.co:unal/868002024-09-06 23:04:41.237Repositorio Institucional Universidad Nacional de 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