Classification of Hyperspectral Satellite Images Using Ensemble Techniques for Object Recognition

Image classification is one of the most important tasks of remote sensing information processing used for object recognition. In this paper, a novel scheme is proposed to improve the accuracy of hyperspectral image classification by amalgamating multiple feature vector sets and ensemble methods with different classifiers. Extracting the texture, color and object features of the satellite images, an ensemble classifier is built for object recognition which recognizes the type of objects present in it. Effective use of feature set and the selection of suitable classification methods with different combination methods are applied for improving classification accuracy. Classifiers such as Multi Layer Perceptron (MLP), k-Nearest Neighbour (KNN) and Support Vector Machine (SVM) are used. This combination shows high performance in terms of Classifier Accuracy (CA), Object Recognition Rate (ORR) and False Alarm Rate (FAR). Results obtained from the ensembling classification give better solution when compared with single classification system. Keywords-Hyperspectral satellite images; ensemble classification; Object Recognition Rate, False Alarm Rate, Classifier Accuracy.