An efficient hierarchical hyperspectral image classification using binary quaternion-moment-preserving thresholding technique

In the study, we propose a novel unsupervised classification technique for hyperspectral images, which consists of two algorithms, referred to as the maximum correlation band clustering (MCBC) and hierarchical binary quaternion-moment-preserving (BQMP) thresholding technique. By the MCBC, we partition the bands into groups and transfer the high-dimensional image data into low-dimensional image features. Afterwards, the hierarchical BQMP approach partitions the feature image into proper regions according to the spectral characteristics. Simulation results performed on AVIRIS images have demonstrated the efficiency of the proposed approaches.