Swarm Intelligence Inspired Classifiers in Comparison with Fuzzy and Rough Classifiers: A Remote Sensing Approach

  • Shelly Bansal
  • Daya Gupta
  • V. K. Panchal
  • Shashi Kumar
Part of the Communications in Computer and Information Science book series (CCIS, volume 40)

Abstract

In recent years the remote sensing image classification has become a global research area for acquiring the geo-spatial information from satellite data. There are soft computing techniques like fuzzy sets and rough sets for remote sensing image classification. This paper presents some optimized approach of image classification of satellite multi spectral images which produces comparable results with the fuzzy or rough set based approach. Here we are presenting a comparison of the image classification by fuzzy set and rough set with the swarm techniques as Ant Colony Optimization(ACO) and Particle Swarm Optimization(PSO). The motivation of this paper is to use the improved swarm computing algorithms for the finding more accuracy in satellite image classification.

Keywords

ACO PSO Remote Sensing Rough Set Fuzzy Set 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shelly Bansal
    • 1
  • Daya Gupta
    • 1
  • V. K. Panchal
    • 2
  • Shashi Kumar
    • 2
  1. 1.Computer Science DepartmentDelhi College of EngineeringDelhi
  2. 2.Defence Terrain Research LabMetcalfe HouseDelhi

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