Hierarchical classification systems for hyperspectral image classification

In this study, we proposed some alternatives for building a binary hierarchical classification (BHC) systems. Two criteria for building the hierarchical tree under the idea of max-cut are addressed and two additional classification architectures based on the constructed trees are also proposed. The performances of these BHC schemes on Indian Pine Site hyperspectral image will be compared by means of using different base classifiers maximum likelihood (ML),support vector machine (SVM) and 1-nearest-neighbor (INN). The experimental results show that the addressed criteria and classification architectures have satisfactory performances.