Performance evaluation of rotation forest for svm-based recursive feature elimination using hyperspectral imagery

Hyperspectral images provide important information for addressing complex classification problems required for a detailed characterization of spectral behavior of the target objects. Classification of such datasets into meaningful land use and land cover classes (LULC) has been the most concentrated topic in remote sensing arena. Rotation forest (RotFor), a new ensemble learning method, has been recently proposed as an alternative to conventional classifiers in the context of multispectral and hyperspectral image classification. In this study, the use of RotFor was investigated for the classification of hyperspectral imagery, specifically an AVIRIS image data. Support vector machine (SVM) was also used as a benchmark classifier. In order to select the best contributing bands of AVIRIS, support vector machine-recursive feature elimination (SVM-RFE) approach was applied. Results of this study showed that RotFor algorithm provided more accurate classification results than the SVM classifier with the use of smaller size data sets selected by SVM-RFE. Based on the Wilcoxon's signed-rank test, the performance difference between RotFor and SVM was found to be statistically significant.