Classification of Hyperspectral Image using Ensemble Learning methods:A comparative study

In this paper, a clean comparison among different ensemble learning methods for hyperspectral image(HSI) classification have been investigated. Random Forest(RF), eXtreme Gradient Boosting(XGBoost) and adaboost have been exploited which extract both spectral and spatial features. Adaboost has been used with two base learners: decision tree(DT) and support vector machine(SVM). The relative gain achieved with inclusion of spatial features along with its spectral counterpart is also investigated. In all cases, adaboost with SVM as base learner delivers the maximum accuracy. The experiments have been performed using two benchmark datasets Indian Pines and Pavia University.