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Research Article | Open Access |

Earth Observation Satellite: Big Data Retrieval Method with Fuzzy Expression of Geophysical Parameters and Spatial Features

Author 1: Kohei Arai
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 14, No. 8 · Published 2023

DOI: 10.14569/IJACSA.2023.0140825

Abstract

A method for fuzzy retrievals of Earth observation satellite image database using geophysical parameters and spatial features is proposed. It is confirmed that the proposed method allows fuzzy expressions of queries with sea surface temperature, chlorophyll-a concentration and cloud coverage as well as circle, line and edge, for instance “rather cold sea surface temperature and a sort of circle feature”. Thus users, in particular, oceanographers may access the most appropriate image data from the database for finding of cold cores (circle features), fronts (arc and line features), etc. in a simple manner. Although this is just an example for oceanographers, it is found that the proposed method allows data mining with fuzzy expressions of geophysical queries from the big data platforms of the earth observation satellite database.

Keywords

How to Cite this Article

Arai, K. (2023). Earth Observation Satellite: Big Data Retrieval Method with Fuzzy Expression of Geophysical Parameters and Spatial Features. International Journal of Advanced Computer Science and Applications, 14(8). https://doi.org/10.14569/IJACSA.2023.0140825

Arai, Kohei. "Earth Observation Satellite: Big Data Retrieval Method with Fuzzy Expression of Geophysical Parameters and Spatial Features." International Journal of Advanced Computer Science and Applications, vol. 14, no. 8, 2023, https://doi.org/10.14569/IJACSA.2023.0140825.

@article{Arai2023,
  title     = {Earth Observation Satellite: Big Data Retrieval Method with Fuzzy Expression of Geophysical Parameters and Spatial Features},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {8},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2023.0140825},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140825}
}

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