Abstract: Genetic algorithm utilizing image clustering with merge and split processes which allows minimizing Fisher distance between clusters is proposed. Through experiments with simulation and real remote sensing satellite imagery data, it is found that the proposed clustering method is superior to the conventional k-means and ISODATA clustering methods in comparison to the geographic maps and classification results from Maximum Likelihood classification method.

Kohei Arai , “Genetic Algorithm Utilizing Image Clustering with Merge and Split Processes Which Allows Minimizing Fisher Distance Between Clusters” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 2(9), 2013. http://dx.doi.org/10.14569/IJARAI.2013.020902

@article{2013,
title = {Genetic Algorithm Utilizing Image Clustering with Merge and Split Processes Which Allows Minimizing Fisher Distance Between Clusters},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2013.020902},
url = {http://dx.doi.org/10.14569/IJARAI.2013.020902},
year = {2013},
publisher = {The Science and Information Organization},
volume = {2},
number = {9},
author = {Kohei Arai }
}