Swarm intelligence for unsupervised classification of hyperspectral images

A new methodology for the unsupervised classification of hyperspectral images is proposed. Based on swarm intelligence, it addresses simultaneously two different issues which are: 1) the estimation of the cluster parameters; and 2) the detection of the best discriminative bands. For such purpose, it optimizes jointly two different criteria, which are the log likelihood function and the Bhattacharyya statistical distance between classes. Experimental results show that, despite the completely unsupervised nature of the proposed methodology, very encouraging performances in terms of classification accuracy can be achieved.