Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6349081 | International Journal of Applied Earth Observation and Geoinformation | 2013 | 12 Pages |
Abstract
⺠We used Landsat imagery to classify land use/cover over a large, highly heterogeneous tropical area. ⺠SVM classifiers outperformed other parametric, non-parametric, and hybrid classifiers. ⺠Textural homogeneity led to the greatest improvements in classification. ⺠SVM classifiers maximized the usefulness of textural homogeneity and attained overall, producer's, and user's accuracies of â¼90%. ⺠Our classification approach seems very well suited to accurately map land use/cover of heterogeneous landscapes.
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Computers in Earth Sciences
Authors
Jaime Paneque-Gálvez, Jean-François Mas, Gerard Moré, Jordi Cristóbal, Martà Orta-MartÃnez, Ana Catarina Luz, Maximilien Guèze, Manuel J. MacÃa, Victoria Reyes-GarcÃa,