Dynamic classifier system for hyperspectral image classification

Abstract

Multiple classifier system (MCS) is one of the effective strategies for hyperspectral image classification. Deploying different dimensionality reduction methods as the input data source to the MCS creates diversity among the base classifiers. The performance of the MCS is guaranteed when the base classifiers are accurate and diverse. Moreover the presence of the bad classifiers may negatively influence the performance of the MCS. In order to form a strong MCS, which are accurate as well as diverse, in this work the dynamic classifier system is developed. The dynamic classifier system selects the adaptive classifier from a pool of classifier for each dimensionality reduction method. The selected classifier relative to each dimensionality reduction method is further combined by different combination functions. Our experimental results on five multi-site hyperspectral images show the potential of dynamic classifier system to increase the classification accuracy significantly.