Otro
Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest
Registro en:
Ieee Transactions On Geoscience And Remote Sensing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 52, n. 10, p. 6075-6085, 2014.
0196-2892
10.1109/TGRS.2013.2294762
WOS:000337173200007
Autor
Pisani, Rodrigo Jose
Mizobe Nakamura, Rodrigo Yuji
Riedel, Paulina Setti
Lopes Zimback, Celia Regina
Falcao, Alexandre Xavier
Papa, João Paulo
Resumen
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. Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)