Title :
Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest
Author :
Pisani, Rodrigo Jose ; Mizobe Nakamura, Rodrigo Yuji ; Setti Riedel, Paulina ; Lopes Zimback, Celia Regina ; Xavier Falcao, Alexandre ; Papa, Joao Paulo
Author_Institution :
Inst. of Geosci. & Exact Sci., Unesp-Univ. Estadual Paulista, Rio Claro, Brazil
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.
Keywords :
Bayes methods; geophysics computing; land cover; pattern classification; pattern clustering; support vector machines; terrain mapping; Bayesian classifier; automatic tools; clustering techniques; k-means; mean shift; pattern recognition techniques; satellite on-board imaging systems; satellite-based land cover classification; supervised optimum-path forest framework; support vector machines; unsupervised situation; Earth; Optimized production technology; Pattern recognition; Prototypes; Remote sensing; Satellites; Training; Land cover classification; optimum-path forest (OPF); remote sensing;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2013.2294762