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.
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Macías-Macías, M., García-Orellana, C.J., González-Velasco, H., Gallardo-Caballero, R. (2003). Independent Component Analysis for Cloud Screening of Meteosat Images. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_70
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