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|Type:||Artigo de periódico|
|Title:||Toward Satellite-based Land Cover Classification Through Optimum-path Forest|
|Abstract:||Land cover classification has been paramount in the last years. Since the amount of information acquired by satellite on-board imaging systems has increased, there is a need for automatic tools that can tackle such problem. Despite the fact that one can find several works in the literature, we propose a novel methodology for land cover classification by means of the optimum-path forest (OPF) framework, which has never been applied to this context up to date. Experiments were conducted in supervised and unsupervised situations against some state-of-the-art pattern recognition techniques, such as support vector machines, Bayesian classifier, k-means, and mean shift. We had shown that supervised OPF can outperform such approaches, being much faster than all. In regard to clustering techniques, all classifiers have achieved similar results. © 1980-2012 IEEE.|
|Editor:||Institute of Electrical and Electronics Engineers Inc.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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