Quasi-Based Hierarchical Clustering for Land Cover Mapping Using Satellite Images

  • J. Senthilnath
  • Ankur raj
  • S. N. Omkar
  • V. Mani
  • Deepak kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 202)

Abstract

This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC).The main objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach—NPSO with quasi distribution(Faure).These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.

Keywords

Niche particle swarm optimization Faure sequence Hierarchical clustering 

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Copyright information

© Springer India 2013

Authors and Affiliations

  • J. Senthilnath
    • 1
  • Ankur raj
    • 2
  • S. N. Omkar
    • 1
  • V. Mani
    • 1
  • Deepak kumar
    • 3
  1. 1.Department of Aerospace EngineeringIndian Institute of ScienceBangaloreIndia
  2. 2.Department of Information TechnologyNational Institute of TechnologySurathkalIndia
  3. 3.Department of Electrical EngineeringIndian Institute of ScienceBangaloreIndia

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