Polarimetric classification of Boreal forest using nonparametric feature selection and multiple classifiers

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.

► The novelty of the paper is fully discussed in Section 1. ► The discussion of the polarimetric features as well as the support vector machine is now shortened. ► The issue of the time gap between the collected reference data and the acquired images is now explained. ► The preprocessing steps in Section 5 are now moved into Section 4. ► The justification for our use of McNemar's test is fully explained in Section 5.