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 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.

• 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
• License: Copyright (c) 2021 Dalila-Mercedes Pachajoa, Héctor Mora-Paz, Dagoberto Mayorca-Torres