Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest
Nenhuma Miniatura disponível
Data
2014-10-01
Autores
Pisani, Rodrigo Jose [UNESP]
Mizobe Nakamura, Rodrigo Yuji [UNESP]
Riedel, Paulina Setti [UNESP]
Lopes Zimback, Celia Regina [UNESP]
Falcao, Alexandre Xavier
Papa, João Paulo [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Ieee-inst Electrical Electronics Engineers Inc
Resumo
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.
Descrição
Palavras-chave
Land cover classification, optimum-path forest (OPF), remote sensing
Como citar
Ieee Transactions On Geoscience And Remote Sensing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 52, n. 10, p. 6075-6085, 2014.