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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Remote Sensing Satellite Image Clustering by Means of Messy Genetic Algorithm

Author 1: Kohei Arai
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 11, No. 3 · Published 2020

DOI: https://doi.org/10.14569/IJACSA.2020.0110338

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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.

Keywords

Genetic Algorithm: GA Messy GA Simple GA clustering introduction

How to Cite this Article

Arai, K. (2020). Remote Sensing Satellite Image Clustering by Means of Messy Genetic Algorithm. International Journal of Advanced Computer Science and Applications, 11(3). https://doi.org/10.14569/IJACSA.2020.0110338

Arai, Kohei. "Remote Sensing Satellite Image Clustering by Means of Messy Genetic Algorithm." International Journal of Advanced Computer Science and Applications, vol. 11, no. 3, 2020, https://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},
  volume    = {11},
  number    = {3},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2020.0110338},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110338}
}

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