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Proceedings Paper

Error prediction of the Gaussian ML classifier in remotely sensed data
Author(s): Chulhee Lee
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Paper Abstract

In this paper, a new method to predict the classification error in the Gaussian ML classifier is proposed. The Gaussian ML classifier is one of the most widely classifiers in pattern classification and remote sensing because of its speed and performance. Several methods have been proposed to estimate error of the Gaussian ML classifier. In particular, the Bhattacharyya distance gives theoretical upper and lower bounds of the classification error. However, in many cases, the bounds ar not tight enough to be useful.In this paper, we proposed a different approach to predict error of the Gaussian ML classifier using the Bhattacharyya distance. We generate two classes with normal distribution and calculate the Bhattacharyya distance and the classification accuracy. The class statistics used to generate data are obtained form real remotely sensed data. We repeat the experiment about 100 million times with different class statistics and try to find the relationship between the classification error and the Bhattacharyya distance empirically. The range of the dimension of the generated data is from 1 to 17. From the experiments, we are able to obtain a formula that gives a much better error estimation of the Gaussian ML classifier. Apparently, it is possible to predict the classification error within 1-2 percent margin.

Paper Details

Date Published: 24 October 1997
PDF: 10 pages
Proc. SPIE 3159, Algorithms, Devices, and Systems for Optical Information Processing, (24 October 1997); doi: 10.1117/12.279445
Show Author Affiliations
Chulhee Lee, Yonsei Univ. (South Korea)


Published in SPIE Proceedings Vol. 3159:
Algorithms, Devices, and Systems for Optical Information Processing
Bahram Javidi; Demetri Psaltis, Editor(s)

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