Comparison of Kernel Functions in the Classification of Irradiance Zones from Multispectral Satellite Images
Due to the growing energy demand and the eminent global warming, there is special interest in the prediction of irradiance based on the reflectance obtained from satellites such as NASA Landsat, since it allows to know where it is more efficient to place photovoltaic receivers. Although there are st...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Pedagógica y Tecnológica de Colombia
- Repositorio:
- RiUPTC: Repositorio Institucional UPTC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uptc.edu.co:001/14327
- Acceso en línea:
- https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13845
https://repositorio.uptc.edu.co/handle/001/14327
- Palabra clave:
- Kernel functions
multispectral satellite images
Landsat
Support Vector Machines
Classification
photovoltaic energy
clasificación
energía fotovoltaica
funciones Kernel
imágenes satelitales multiespectrales
Landsat
máquinas de soporte vectorial
- Rights
- License
- Copyright (c) 2021 Dalila-Mercedes Pachajoa, Héctor Mora-Paz, Dagoberto Mayorca-Torres
| Summary: | Due to the growing energy demand and the eminent global warming, there is special interest in the prediction of irradiance based on the reflectance obtained from satellites such as NASA Landsat, since it allows to know where it is more efficient to place photovoltaic receivers. Although there are studies for obtaining regression models with alternative Kernel functions, their performance for classification models is unknown and it is here where this research focuses. The study couples alternative Kernel functions to the support vector machines (SVM) algorithm for classification problems, where the best configuration for these algorithms is explored to finally obtain a set of irradiance maps zoned by class. |
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