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

Author(s)

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]

Affiliation

^[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

• 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

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. (AU)

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