Classification of Hyperspectral Remote Sensing Images by an Ensemble of Support Vector Machines Under Imbalanced Data

Abstract

It is found very often that training data contains unequal number of representative samples for classes. Some of the classes might be represented by a larger number of samples while the rest with lower number of samples. Classification of remote sensing images with imbalanced class distribution could result in a significant drawback in the classification performance attainable by most standard classifier learning algorithms which assume a relatively balanced class distribution and equal misclassification costs. So it is worth exploring if ensemble method could give an improved performance under the condition of imbalanced training data. In the proposed work, Support Vector Machine (SVM) is used as base classifiers in the ensemble committee. An ensemble of SVMs will be constructed using popular Bagging method. Standard Hyperspectral data such as Salinas is used as test data. The proposed work will explore the efficiency of ensemble technique in improving classification accuracy, even in cases of robust classifier such as SVM.