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

• Published in: IEEE transactions on geoscience and remote sensing Vol. 52; no. 10; pp. 6075 - 6085
• Main Authors: Pisani, Rodrigo Jose, Mizobe Nakamura, Rodrigo Yuji, Setti Riedel, Paulina, Lopes Zimback, Celia Regina, Xavier Falcao, Alexandre, Papa, Joao Paulo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayesian analysis; Classification; Classifiers; Earth; Forests; Land cover; Land cover classification; Optimized production technology; optimum-path forest (OPF); Pattern recognition; Prototypes; Remote sensing; Satellites; State of the art; Support vector machines; Training; remote sensing; optimum-path forest (OPF); Land cover classification
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2013.2294762

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.