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UNSUPERVISED CLASSIFICATION OF REMOTELY SENSED IMAGES USING GAUSSIAN MIXTURE MODELS AND PARTICLE SWARM OPTIMIZATION
 

Summary: UNSUPERVISED CLASSIFICATION OF REMOTELY SENSED IMAGES USING
GAUSSIAN MIXTURE MODELS AND PARTICLE SWARM OPTIMIZATION
C aglar Ari
Department of Electrical and Electronics Engineering
Bilkent University
Bilkent, 06800, Ankara, Turkey
cari@ee.bilkent.edu.tr
Selim Aksoy
Department of Computer Engineering
Bilkent University
Bilkent, 06800, Ankara, Turkey
saksoy@cs.bilkent.edu.tr
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.

  

Source: Aksoy, Selim - Department of Computer Engineering, Bilkent University

 

Collections: Computer Technologies and Information Sciences