Pisani, Rodrigo Jose [UNESP]Mizobe Nakamura, Rodrigo Yuji [UNESP]Riedel, Paulina Setti [UNESP]Lopes Zimback, Celia Regina [UNESP]Falcao, Alexandre XavierPapa, João Paulo [UNESP]2015-03-182015-03-182014-10-01Ieee Transactions On Geoscience And Remote Sensing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 52, n. 10, p. 6075-6085, 2014.0196-2892http://hdl.handle.net/11449/117074Land 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.6075-6085engLand cover classificationoptimum-path forest (OPF)remote sensingArtigo10.1109/TGRS.2013.2294762WOS:000337173200007Acesso restrito9039182932747194