Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IGARSS.2009.5417723 | IGARSS |
Keywords | Field | DocType |
geophysical image processing,image classification,optimisation,pattern clustering,Bhattacharyya statistical distance,cluster parameter estimation,hyperspectral images,log likelihood function,swarm intelligence,unsupervised classification,Feature selection,hyperspectral images,image clustering,k-means algorithm,multiobjective optimization,particle swarm optimization (PSO) | Bhattacharyya distance,Computer science,Swarm intelligence,Artificial intelligence,Cluster analysis,Contextual image classification,Discriminative model,Particle swarm optimization,Computer vision,k-means clustering,Pattern recognition,Hyperspectral imaging,Machine learning | Conference |
Volume | ISSN | Citations |
5 | 2153-6996 | 1 |
PageRank | References | Authors |
0.35 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Paoli | 1 | 212 | 16.73 |
| Farid Melgani | 2 | 1100 | 80.98 |
| Edoardo Pasolli | 3 | 285 | 17.04 |