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Satellite images have been extensively used for rainfall estimation predictive models based on pattern recognition techniques, even so unsupervised and supervised. However, most of these kind of data are unlabeled, and the acquisition of labeled data for a learning problem often requires a skilled human agent to manually classify training examples. In this paper we introduce the use of semi-supervised...
We investigate multispectral satellite image classification using the neural model previously proposed by the first author called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of self-organizing neural network modules. For comparison, we evaluate the performances of several statistical classifiers (Bayes, 1-NN, and K-means). The implemented neural versus statistical...
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