Swarm intelligence inspired classifiers in comparison with fuzzy and rough classifiers: A remote sensing approach

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. © 2009 Springer Berlin Heidelberg.