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DOI: 10.14569/IJACSA.2020.0110464
PDF

Image Classification Considering Probability Density Function based on Simplified Beta Distribution

Author 1: Kohei Arai

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 4, 2020.

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Abstract: Method for image classification considering Probability Density Function (PDF) based on simplified beta distributions is proposed. In this paper, image classification for Synthetic Aperture Radar (SAR) data is concerned. In particular, Probability Density Function (PDF) of SAR data is followed by not multivariate normal distribution but Chi-Square like distribution. It, however, is not always true that the PDF of SAR data is followed by Chi-Square distribution. Due to the mismatch between Chi-Square distribution and actual distribution, classification performance gets worth. In this paper, simplified beta distribution is assumed for the PDF of the SAR data. Furthermore, it is used to add texture information to the SAR data when the Maximum Likelihood classification is applied. In the paper, “Contrast” of texture feature is added to the SAR data. Through the experiments with real SAR data, it is found that matching error between real PDF and the proposed simplified beta distribution is smaller than the normal distribution. It is also found that applying the proposed distribution-adaptive maximum likelihood method using the simplified beta-distribution could achieve a classification accuracy improvement of 94.7% and 12.1%.

Keywords: Synthetic aperture radar (SAR); maximum likelihood classification: MLH; probability density function (PDF); simplified beta distribution

Kohei Arai, “Image Classification Considering Probability Density Function based on Simplified Beta Distribution” International Journal of Advanced Computer Science and Applications(IJACSA), 11(4), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110464

@article{Arai2020,
title = {Image Classification Considering Probability Density Function based on Simplified Beta Distribution},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110464},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110464},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {4},
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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