A pairwise decision tree framework for hyperspectral classification

Abstract

Abstract A novel pairwise decision tree (PDT) framework is proposed for hyperspectral classification, where no partitions and clustering are needed and the original C‐class problem is divided into a set of two‐class problems. The top of the tree includes all original classes. Each internal node consists of either a set of class pairs or a set of class pairs and a single class. The pairs are selected by the proposed sequential forward selection (SFS) or sequential backward selection (SBS) algorithms. The current node is divided into next‐stage nodes by excluding either class of each selected pair. In the classification, an unlabelled pixel is recursively classified into the next node, by excluding the less similar class of each node pair until the classification result is obtained. Compared to the single‐stage classifier approach, the pairwise classifier framework and the binary hierarchical classifier (BHC), experiments on an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set for a nine‐class problem demonstrated the effectiveness of the proposed framework. Acknowledgements We thank Dr D. Landgrebe of Purdue University, West Lafayette, IN, USA, for providing the AVIRIS data set and the documents.