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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest

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Pisani, Rodrigo Jose [1] ; Mizobe Nakamura, Rodrigo Yuji [2] ; Riedel, Paulina Setti [1] ; Lopes Zimback, Celia Regina [3] ; Falcao, Alexandre Xavier [4] ; Papa, Joao Paulo [2]
Total Authors: 6
[1] Unesp Univ Estadual Paulista, Inst Geosci & Exact Sci, BR-13506900 Rio Claro - Brazil
[2] Unesp Univ Estadual Paulista, Dept Comp Sci, BR-17040 Bauru - Brazil
[3] Unesp Univ Estadual Paulista, Sch Agron Sci, BR-18618970 Botucatu, SP - Brazil
[4] Unicamp Univ Campinas, Inst Comp, BR-13083859 Campinas, SP - Brazil
Total Affiliations: 4
Document type: Journal article
Source: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING; v. 52, n. 10, p. 6075-6085, OCT 2014.
Web of Science Citations: 9

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. (AU)

FAPESP's process: 09/16206-1 - New trends on optimum-path forest-based pattern recognition
Grantee:João Paulo Papa
Support type: Research Grants - Young Investigators Grants