Weight-Based Rotation Forest for Hyperspectral Image Classification

Abstract

In this letter, we propose a new weight-based rotation forest (WRoF) induction algorithm for the classification of hyperspectral image. The main idea of the new method is to guide the growth of trees adaptively via exploring the potential of important instances. The importance of a training instance is reflected by a dynamic weight function. The higher the weight of an instance, the more the next tree will have to focus on the instance. Experimental results on two real hyperspectral data sets show that the WRoF algorithm results in significant classification improvement compared with random forests and rotation forest.