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. ©2009 IEEE.
Swarm intelligence for unsupervised classification of hyperspectral images / Paoli, A.; Melgani, F.; Pasolli, E.. - 5:(2009), pp. 96-99. (Intervento presentato al convegno 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 tenutosi a Cape Town, zaf nel 2009) [10.1109/IGARSS.2009.5417723].
Swarm intelligence for unsupervised classification of hyperspectral images
Pasolli E.
2009
Abstract
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. ©2009 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
