Independent Component Analysis for Cloud Screening of Meteosat Images

Authors:
Miguel Macías-Macías;Carlos J. García-Orellana;Horacio González-Velasco;Ramón Gallardo-Caballero
Affiliations:
Departamento de Electrónica e Ingeniería Electromecánica, Universidad de Extremadura, Badajoz, Spain 06071;Departamento de Electrónica e Ingeniería Electromecánica, Universidad de Extremadura, Badajoz, Spain 06071;Departamento de Electrónica e Ingeniería Electromecánica, Universidad de Extremadura, Badajoz, Spain 06071;Departamento de Electrónica e Ingeniería Electromecánica, Universidad de Extremadura, Badajoz, Spain 06071
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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Abstract

In this work we use Independent Component Analysis (ICA) as feature extraction stage for cloud screening of Meteosat images covering the Iberian Peninsula. The images are segmented in the classes land (L), sea (S), fog (F), low clouds (CL), middle clouds (CM), high clouds (CH) and clouds with vertical growth (CV). The classification of the pixels of the images is performed with a back propagation neural network (BPNN) from the features extracted by applying the FastICA algorithm over 3x3, 5x5 and 7x7 pixel windows of the images.