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DOI: 10.14569/IJARAI.2016.050101
PDF

Comparative Study of Optimization Methods for Estimation of Sea Surface Temperature and Ocean Wind with Microwave Radiometer Data

Author 1: Kohei Arai

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 5 Issue 1, 2016.

  • Abstract and Keywords
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Abstract: Comparative study of optimization methods for estimation sea surface temperature and ocean wind with microwave radiometer data is conducted. The well known mesh method (Grid Search Method: GSM), regressive method, and simulated annealing method are compared. Surface emissivity is estimated with the simulated annealing and compared to the well known Thomas T. Wilheit model based emissivity. On the other hand, brightness temperature of microwave radiometer as a function of observation angle is estimated by the simulated annealing method and compares it to the actual microwave radiometer data. Also, simultaneous estimation of sea surface temperature and ocean wind speed is carried out by the simulated annealing and compared it to the estimated those by the GSM method. The experimental results show the simulated annealing which allows estimation of global optimum is superior to the other method in some extent.

Keywords: Microwave radiometer; remote sensing; sea surface temperature; nonlinear optimization theory; simulated annealing

Kohei Arai, “Comparative Study of Optimization Methods for Estimation of Sea Surface Temperature and Ocean Wind with Microwave Radiometer Data” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 5(1), 2016. http://dx.doi.org/10.14569/IJARAI.2016.050101

@article{Arai2016,
title = {Comparative Study of Optimization Methods for Estimation of Sea Surface Temperature and Ocean Wind with Microwave Radiometer Data},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2016.050101},
url = {http://dx.doi.org/10.14569/IJARAI.2016.050101},
year = {2016},
publisher = {The Science and Information Organization},
volume = {5},
number = {1},
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.

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