2026-10-15T10:43:34Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/63222024-05-14T11:29:23Zcom_10259_6164com_10259_4534com_10259.4_106com_10259_2604com_10259_6229com_10259_6231com_10259_4266com_10259_4393com_10259_5086col_10259_6165col_10259_6230col_10259_6232col_10259_4394
Repositorio Institucional de la Universidad de Burgos
author
Granados López, Diego
author
García Rodríguez, Ana
author
García Rodríguez, Sol
author
Suárez García, Andrés
author
Diez Mediavilla, Montserrat
author
Alonso Tristán, Cristina
2022-01-11T10:29:25Z
2022-01-11T10:29:25Z
2021-12
1076-2787
http://hdl.handle.net/10259/6322
10.1155/2021/2636157
Digital sky images are studied for the definition of sky conditions in accordance with the CIE Standard General Sky Guide. Likewise, adequate image-processing methods are analyzed that highlight key image information, prior to the application of Artificial Neural Network classification algorithms. Twenty-two image-processing methods are reviewed and applied to a broad and unbiased dataset of 1500 sky images recorded in Burgos, Spain, over an extensive experimental campaign. The dataset comprises one hundred images of each CIE standard sky type, previously classified from simultaneous sky scanner data. Color spaces, spectral features, and texture filters image-processing methods are applied. While the use of the traditional RGB color space for image-processing yielded good results (ANN accuracy equal to 86.6%), other color spaces, such as Hue Saturation Value (HSV), which may be more appropriate, increased the accuracy of their global classifications. The use of either the green or the blue monochromatic channels improved sky classification, both for the fifteen CIE standard sky types and for simpler classification into clear, partial, and overcast conditions. The main conclusion was that specific image-processing methods could improve ANN-algorithm accuracy, depending on the image information required for the classification problem.
eng
Atribución 4.0 Internacional
Pixel-Based Image Processing for CIE Standard Sky Classification through ANN
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/6322/1/Granados-Complexity_2021.pdf
File
MD5
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application/pdf
Granados-Complexity_2021.pdf