Hierarchical clustering approach for unsupervised image classification of hyperspectral data

A multistage hierarchical clustering technique, which is an unsupervised technique, has been proposed in this paper for classifying the hyperspectral data. The multistage algorithm consists of two stages. The "local" segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. The "global" segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using a context-free similarity measure. This study applied the multistage hierarchical clustering method to the data generated by band reduction, band selection and data compression. The classification results were compared with them using full bands.