Unsupervised classification of remotely sensed images using Gaussian mixture models and particle swarm optimization
Author
Arı, Çağlar
Aksoy, Selim
Date
2010Source Title
2010 IEEE International Geoscience and Remote Sensing Symposium
Publisher
IEEE
Pages
1859 - 1862
Language
English
Type
Conference PaperItem Usage Stats
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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. However, 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 algorithm 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. © 2010 IEEE.
Keywords
ClusteringGaussian Mixture Model
Parametrizations
Particle swarm
Stochastic search
Blind source separation
Covariance matrix
Estimation
Gaussian distribution
Geology
Maximum likelihood estimation
Optical communication
Particle swarm optimization (PSO)
Remote sensing
Stochastic models
Stochastic systems
Clustering algorithms