Handling the Class Imbalance in Land-Cover Classification Using Bagging-Based Semisupervised Neural Approach

Abstract

In this letter, a semisupervised neural network-based approach has been proposed for handling the class-imbalance problem in land-cover classification under a hybrid integration of selective undersampling, oversampling, and a bagging-based ensemble of classifiers. Here, a selective undersampling technique is utilized so as to minimize the loss of information from the majority classes; whereas, the minority class sizes are simultaneously increased by exploiting their presence in the unlabeled test samples. Finally, the imbalanced original training set along with the newly found minority samples is used to classify the remaining unlabeled samples from the test set. Experiments conducted on the patterns collected from multispectral (high as well as very high resolution images) and hyperspectral remote sensing satellite images show encouraging performance of the proposed scheme when compared to other state-of-the-art techniques.