A Satellite image classification is a significant method used in remote sensing for the automated analysis and pattern recognition of satellite data, which facilitate the automated understanding of a large amount of information. These days, there exist many types of classification algorithms, such as parallelepiped and minimum distance classifiers, but it is still essential to get better their performance in terms of correctness rate. Alternatively, over the last few years, cellular automata have been utilized in remote sensing to implement procedure related to simulation. While there is little preceding research of cellular automata related to satellite image classification, they offer much reward that can improve the results of classical categorization algorithms. In this thesis, we are applying a Swarm intelligence based hybrid algorithm of Ant Colony Optimization and Genetic Algorithm Optimization (GA) to execute the satellite picture classification. On merging them, they give an immense impact that is very significant in remote sensing application. The outcomes generated through the hybrid algorithm are contrasted with the outcome attained by means of other swarm intelligence to demonstrate the proofs of our proposed hybrid algorithm. The test of hybrid method is accomplished by categorizing a multi-spectral, high resolution satellite picture of Alwar region.
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Image classification, Multispectral image classification, remote sensing, Swarm intelligence, Ant Colony Optimization algorithm, Genetic Optimization Algorithm.
