Feature Separation Based Rotation Forest for Hyperspectral Image Classification

The classification is one of the most important tasks of the hyperspectral remote sensing. However, the task always suffers from the curse of dimensionality which makes most classifier models disabled. In this paper, a novel ensemble method named feature separation based rotation forest (FSRoF) is proposed to avoid the influence of high-dimensionality by training a series of independent classifiers with the datasets in a low-dimensionality rotation space and using the out-of-bag instances to select the base classifiers of high quality to construct the final ensemble model. The random forest (RF) and the traditional rotation forest (RoF) are adopted as the comparisons in our experiment.