Abstract
In this work we tackle a particular case of image segmentation, the automatic detection of the amount and type of clouds over the Iberian Peninsula using satellite images. To segment the images we classify each pixel of the image into one of the classes defined using a neural network and a set of features representative of the pixel. We emphasized in the preprocessing stage, extracting and selecting a suitable set of features from the images to carry out an optimal classification. To carry out the feature extraction we use the independent component analysis (ICA) algorithm. The features extracted with this algorithm are very dependent on the dimension of the patches, so we extract several sets of features, one for each value of the dimension of the patch. All of these sets of features are joined together to form an initial characteristic vector of the pixels of the images. Finally, we reduce the dimensionality of this initial characteristic vector by means of Genetic Algorithms (GA), choosing the best subset of features that offer the best classification results.
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Macías-Macías, M., García-Orellana, C.J., González-Velasco, H., Gallardo-Caballero, R. (2005). ICA and GA Feature Extraction and Selection for Cloud Classification. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_53
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DOI: https://doi.org/10.1007/11551188_53
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