Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest - 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] (Ieee-inst Electrical Electronics Engineers Inc, 2014)
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.
Keywords: Land cover classification; optimum-path forest (OPF); remote sensing