The SAS Image Data Classification with Minimum Error Probability Bayes Classifier

Wei Lu; Jing Wang; Jing Guan; Heng Yang

doi:10.4028/www.scientific.net/AMM.263-266.2576

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 263-266The SAS Image Data Classification with Minimum...

The SAS Image Data Classification with Minimum Error Probability Bayes Classifier

Abstract:

Due to multipath noise pollution, SAS image consists of two parts : target and noise .They can be described by K + K mixture distribution . How to separate noise data which obey K distribution from the target which also obeys K is a hot topic in SAS image field. This paper used the minimum error probability Bayes classifier to solve this problem, and achieved good results. At the same time, this paper also studied the factors that affect the classification results, such as the absolute value difference of training sample parameters and K distribution parameters.

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Periodical:

Applied Mechanics and Materials (Volumes 263-266)

Pages:

2576-2580

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.263-266.2576

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Online since:

December 2012

Authors:

Wei Lu, Jing Wang, Jing Guan, Heng Yang

Keywords:

K+K Distribution, Minimum Error Probability Bayes Classifier, SAS Image

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References

[1] I.P. Kirsteins.Blind separation of signal and reverberation by minimizing the circular variance of the phase[C].Proc.of Asilomar 2002 Conference,Nov.2002,2:1664-1668

DOI: 10.1109/acssc.2002.1197059

Google Scholar

[2] Piper J.E. Lim,R.Thorsos,Williams. Buried Sphere Detection Using a Synthetic Aperture Sonar. IEEE Oceanic Engineering Society 34（4）：485-494. (2009)

DOI: 10.1109/joe.2009.2030971

Google Scholar

[3] Ma_lgorzata Plechawska1, Lukasz Wojcik, Andrzej Polanski. Numerical analysis of EM estimation of mixturemodel parameters (2009)

Google Scholar

[4] I.P. Kirsteins.Blind separation of signal and reverberation by minimizing the circular variance of the phase[C].Proc.of Asilomar 2002 Conference,Nov.2002,2:1664-1668

DOI: 10.1109/acssc.2002.1197059

Google Scholar

[5] Husam Awni Bayoud.Bayes and Empirical Bayes Estimation of the Parameter n in a Bionmial Distribution.Communications in statistics.B,Simulation and compution 2011,40(8/10)

DOI: 10.1080/03610918.2010.526737

Google Scholar