Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/87901
Type: Artigo de periódico
Title: Toward Satellite-based Land Cover Classification Through Optimum-path Forest
Author: Pisani R.J.
Nakamura R.Y.M.
Riedel P.S.
Zimback C.R.L.
Falcao A.X.
Papa J.P.
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.
Rights: fechado
Identifier DOI: 10.1109/TGRS.2013.2294762
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84902077626&partnerID=40&md5=3b862ae98f4d7b101194da01b3936a5d
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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