期刊
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
卷 19, 期 -, 页码 139-150出版社
ELSEVIER
DOI: 10.1016/j.jag.2012.04.015
关键词
PolSAR data; Forest; Classification; Feature selection; Leaf-on; Leaf-off
资金
- Natural Sciences and Engineering Council of Canada
Polarimetric SAR data contains a large amount of potential information that may be used to characterize forested scenes. However, the large number of PolSAR parameters and discriminators cannot all be used in most classification problems. Some form of feature selection will improve classification results and improve the efficiency of the system. In addition, classification of PolSAR data may be improved with an ensemble of classifiers, each tuned to a different class. Our research is in the Petawawa experimental forest, in the boreal forest northwest of Ottawa, Ontario, Canada. We employ Radarsat-2 fine-quad image data acquired in August (leaf-on) and November (leaf-off) of 2009. We present two system designs in this paper. The first system consists of a feature selector based on a non-parametric evaluation function and a support vector machine for classification. We demonstrate that the feature selection step improves classification accuracy significantly over a baseline classifier. We then present a system consisting of an ensemble of SVM classifiers, each with its own feature selection component and trained on an individual class. The classifier likelihoods are combined in a final step. We demonstrate that this system improves classification accuracy significantly over a single-classifier system. Finally, we demonstrate that classification accuracies are significantly higher when leaf-on and leaf-off images are combined over a single season image. (c) 2012 Elsevier B.V. All rights reserved.
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