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)


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.


Niche particle swarm optimization Faure sequence Hierarchical clustering 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. David, L.: Hyperspectral image data analysis as a high dimensional signal processing problem. IEEE Signal processing Mag. 19 (1), 17–28 (2002)Google Scholar
  2. Senthilnath, J., Omkar, S.N., Mani, V., Tejovanth, N., Diwakar, P.G., Shenoy, A.B.: Hierarchical clustering algorithm for land cover mapping using satellite images. IEEE journal of selected topics in applied earth observations and remote sensing. 5 (3), 762-768 (2012)Google Scholar
  3. Brits, R., Engelbrecht, A.P., van den Bergh, F.: A niching Particle Swarm Optimizer. In proceedings of the fourth Asia Pacific Conference on Simulated Evolution and learning. 692 –696 (2002)Google Scholar
  4. Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization in noisy and continuously changing environments. in Proceedings of International Conference on Artificial Intelligence and soft computing. 289-294 (2002)Google Scholar
  5. Kimura, S., Matsumura, K.: Genetic Algorithms using low discrepancy sequences. in proc of GEECO. 1341 –1346 (2005)Google Scholar
  6. Brits, R., Engelbrecht, A.P., van den Bergh, F.: Solving systems of unconstrained equations using particle swarm optimization. in proceedings of the IEEE Conference on Systems. Man and Cybernetics. 3, 102–107 (2002)Google Scholar
  7. Nguyen, X.H., Mckay, R.I., Tuan, P.M.: Initializing PSO with Randomized Low-Discrepancy Sequences: The Comparative Results, In Proc. of IEEE Congress on Evolutionary Algorithms. 1985–1992 (2007)Google Scholar
  8. Schwarz, G.: Estimating the dimension of a model. the Annals of statistics. 6 (2), 461-464 (1978)Google Scholar
  9. Donald, K.: Chapter 3 – Random Numbers”. The Art of Computer Programming. Seminumerical algorithms (3 ed.) (1997)Google Scholar
  10. Niederreiter, H.: Quasi-Monte Carlo Methods and Pseudo Random Numbers. Bulletin of American Mathematical Society. 84(6) 957-1041 (1978)Google Scholar
  11. Marco A.G.D.: Quasi-Monte Carlo Simulation
  12. Galanti, S., Jung, A.: Low-Discrepancy Sequences: Monte Carlo Simulation of Option Prices. Journal of Derivatives. 63-83 (1997)Google Scholar
  13. Comaniciu, D., Meer, P.: Mean shift :a robust approach towards feature space analysis. IEEE Trans.pattern Anal.machIntell. 24 (5), 603-619 (2002)Google Scholar
  14. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proceedings of the IEEEGoogle Scholar
  15. International Conference on Neural Networks, IV (Piscataway, NJ), IEEE Service Center. 1942–1948 (1995)Google Scholar
  16. Senthilnath, J., Omkar, S.N., Mani, V., Tejovanth, N., Diwakar, P.G., Archana, S.B.: Multi-spectral satellite image classification using glowwarm swarm optimization. in proc. IEEE int. Geoscience and Remote Sensing Symp (IGARSS).47-50 (2011)Google Scholar
  17. Li, H., Zang, K., Jiang,T.: The regularized EM algorithm. in proc.20thNat.conf.Artificial Intelligence. 807-8 (2005)Google Scholar
  18. MacQueen,J.: Some methods for classification and analysis of multi-variate observations. in proc .5th BerkeleySymp. 281-297 (1967)Google Scholar
  19. Senthilnath, J., Omkar, S. N., Mani, V.: Clustering using firefly algorithm – Performance study. Swarm and Evolutionary Computation. 1 (3), 164-171 (2011)Google Scholar
  20. Suresh, S., Sundararajan, N., Saratchandran, P.: A sequential multi-category classifier using radial basis function networks. Neurocomputing. 71, 1345-1358 (2008)Google Scholar

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

Personalised recommendations