Name
Title | ||
|---|---|---|
Unsupervised classification of remotely sensed images using Gaussian mixture models and particle swarm optimization |
Abstract | ||
|---|---|---|
Gaussian mixture models (GMM) are widely used for un- supervised classification applications in remote sensing. Expectation-Maximization (EM) is the standard algorithm employed to estimate the parameters of these models. How- ever, such iterative optimization methods can easily get trapped into local maxima. Researchers use population- based stochastic search algorithms to obtain better estimates. We present a novel particle swarm optimization-based al- gorithm for maximum likelihood estimation of Gaussian mixture models. The proposed approach provides solutions for important problems in effective application of population- based algorithms to the clustering problem. We present a new parametrization for arbitrary covariance matrices that allows independent updating of individual parameters during the search process. We also describe an optimization formulation for identifying the correspondence relations between different parameter orderings of candidate solutions. Experiments on a hyperspectral image show better clustering results compared to the commonly used EM algorithm for estimating GMMs. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IGARSS.2010.5653855 | IGARSS |
Keywords | Field | DocType |
covariance matrices,iterative methods,maximum likelihood estimation,particle swarm optimisation,remote sensing,unsupervised learning,Gaussian mixture models,covariance matrices,expectation-maximization,iterative optimization methods,maximum likelihood estimation,particle swarm optimization,remotely sensed images,unsupervised classification,Gaussian mixture models,clustering,covariance parametrization,maximum likelihood estimation,particle swarm optimization,stochastic search | Particle swarm optimization,Search algorithm,Pattern recognition,Computer science,Expectation–maximization algorithm,Multi-swarm optimization,Unsupervised learning,Artificial intelligence,Cluster analysis,Mixture model,Metaheuristic | Conference |
ISSN | Citations | PageRank |
2153-6996 | 2 | 0.38 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caglar Ari | 1 | 14 | 1.10 |
| Selim Aksoy | 2 | 621 | 51.11 |