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      • Department of Electrical and Electronics Engineering
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      Unsupervised classification of remotely sensed images using Gaussian mixture models and particle swarm optimization

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      Author
      Arı, Çağlar
      Aksoy, Selim
      Date
      2010
      Source Title
      2010 IEEE International Geoscience and Remote Sensing Symposium
      Publisher
      IEEE
      Pages
      1859 - 1862
      Language
      English
      Type
      Conference Paper
      Item 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
      Clustering
      Gaussian 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
      Permalink
      http://hdl.handle.net/11693/28481
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/IGARSS.2010.5653855
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      • Department of Computer Engineering 1368
      • Department of Electrical and Electronics Engineering 3524
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