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An Unsupervised Classification Method of Remote Sensing Images Based on Ant Colony Optimization Algorithm

  • Duo Wang
  • Bo Cheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6440)

Abstract

Remote sensing images classification method can be divided into supervised classification and unsupervised classification according to whether there is prior knowledge. Supervised classification is a machine learning procedure for deducing a function from training data; unsupervised classification is a kind of classification which no training sample is available and subdivision of the feature space is achieved by identifying natural groupings present in the images values. As a branch of swarm intelligence, ant colony optimization algorithm has self-organization, adaptation, positive feedback and other intelligent advantages. In this paper, ant colony optimization algorithm is tentatively introduced into unsupervised classification of remote sensing images. A series of experiments are performed with remote sensing data. Comparing with the K-mean and the ISODATA clustering algorithm, the experiment result proves that artificial ant colony optimization algorithm provides a more effective approach to remote sensing images classification.

Keywords

unsupervised classification pheromone data discretization ant colony optimization algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Duo Wang
    • 1
    • 2
  • Bo Cheng
    • 1
  1. 1.Center for Earth Observation and Digital Earth Chinese Academy of SciencesChina
  2. 2.Graduate University of Chinese Academy of Sciences (GUCAS)China

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