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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 3, 2020.
Abstract: Messy Genetic Algorithm (GA) is applied to the satellite image clustering. Messy GA allows to maintain a long schema, due to the fact that schema can be expressed with a variable length of codes, so that more suitable cluster can be found in comparison to the existing Simple GA clustering. The results with simulation data show that the proposed Messy GA based clustering shows four times better cluster separability in comparison to the Simple GA while the results with Landsat TM data of Saga show almost 65% better clustering performance.
Kohei Arai, “Remote Sensing Satellite Image Clustering by Means of Messy Genetic Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 11(3), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110338
@article{Arai2020,
title = {Remote Sensing Satellite Image Clustering by Means of Messy Genetic Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110338},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110338},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {3},
author = {Kohei Arai}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.