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Evaluating the Potential of Particle Swarm Optimization for Hyperspectral Image Clustering in Minimum Noise Fraction Feature Space

  • Shahin Rahmatollahi Namin
  • Amin Alizadeh Naeini
  • Farhad Samadzadegan
Conference paper
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 61)

Abstract

Particle Swarm Optimization methods are the optimization techniques inspired by the social movements of animals. In these methods, the particles movements are based on simple rules, but make complex overall behavior and search of the space. The clustering is the process of dividing the existing data to diverse groups based on the inherent characteristics and similarity of them and can also be seen as an optimization problem. Due to the complexity of this problem in hyperspectral remotely sensed data, different feature space and clustering techniques are applied and evaluated in this field. In this paper, a PSO clustering algorithm is evaluated in Minimum noise fraction space for hyperspectral AVIRIS image taken over the northwest indiana’s indian pine agricultural land. The comparison of the results with the K-means clustering method shows better obtained performance for the PSO clustering in minimum noise fraction feature space.

Keywords

Particle Swarm Optimization Feature Space Cluster Center Particle Swarm Optimization Algorithm Hyperspectral Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Shahin Rahmatollahi Namin
    • 1
  • Amin Alizadeh Naeini
    • 1
  • Farhad Samadzadegan
    • 1
  1. 1.Department of Surveying Engineering, College of EngineeringUniversity of TehranTehranIran

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