Name
Title | ||
|---|---|---|
Clustering of Hyperspectral Images Based on Multiobjective Particle Swarm Optimization |
Abstract | ||
|---|---|---|
In this paper, we present a new methodology for clustering hyperspectral images. It aims at simultaneously solving the following three different issues: 1) estimation of the class statistical parameters; 2) detection of the best discriminative bands without requiring the a priori setting of their number by the user; and 3) estimation of the number of data classes characterizing the considered imag... |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TGRS.2009.2023666 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Hyperspectral imaging,Particle swarm optimization,Hyperspectral sensors,Clustering algorithms,Clustering methods,Remote monitoring,Image analysis,Analytical models,Read only memory,Availability | Particle swarm optimization,k-means clustering,Bhattacharyya distance,Pattern recognition,Minimum description length,Hyperspectral imaging,Multi-swarm optimization,Artificial intelligence,Contextual image classification,Cluster analysis,Mathematics | Journal |
Volume | Issue | ISSN |
47 | 12 | 0196-2892 |
Citations | PageRank | References |
56 | 1.67 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Paoli | 1 | 212 | 16.73 |
| Farid Melgani | 2 | 1100 | 80.98 |
| Edoardo Pasolli | 3 | 285 | 17.04 |