Projected clustering of hyperspectral imagery using region merging

In this study, a novel method for clustering hyperspectral images is proposed. The proposed method performs projected clustering in feature/spectral space and merges regions in image/spatial space. The novelty of the proposed method lies in the way in which spectral and spatial information is used, along with its inclusion in the projected clustering framework. The proposed method transfers clusters formed in feature space to image space by converting them into regions. Then in image space, regions are iteratively merged by making use of spatial adjacency and spectral similarity. To evaluate the effectiveness of the proposed method, experiments are conducted on three hyperspectral images. The proposed method is also compared with other partitional clustering methods. Results demonstrate that the proposed method has ability to achieve better performance in most cases.