SAR Image Recognition Method with Poisson Gamma Belief Network Model

GUO Dandan; CHEN Bo; CONG Yulai; WEN Wei

doi:10.11999/JEIT161068

Volume 38 Issue 12

Jan. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(12): 2996-3003

GUO Dandan, CHEN Bo, CONG Yulai, WEN Wei. SAR Image Recognition Method with Poisson Gamma Belief Network Model[J]. Journal of Electronics & Information Technology, 2016, 38(12): 2996-3003. doi: 10.11999/JEIT161068

Citation:

GUO Dandan, CHEN Bo, CONG Yulai, WEN Wei. SAR Image Recognition Method with Poisson Gamma Belief Network Model[J]. Journal of Electronics & Information Technology, 2016, 38(12): 2996-3003. doi: 10.11999/JEIT161068

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SAR Image Recognition Method with Poisson Gamma Belief Network Model

doi: 10.11999/JEIT161068

GUO Dandan¹,
CHEN Bo^{1
,
,},
CONG Yulai¹,
WEN Wei¹

Funds:

The National Natural Science Foundation of China (61372132, 61271291), The Program for New Century Excellent Talents (NCET13-0945), The National Science Fund for Distinguished Young Scholars (61525105), The Program for Young Thousand Talent by Chinese Central Government

Received Date: 2016-10-12
Rev Recd Date: 2016-12-02
Publish Date: 2016-12-19

Abstract

Abstract

Feature extraction is a key step and difficult point in SAR image target recognition. This paper presents a novel method based on Poisson Gamma Belief Network (PGBN) for SAR image target recognition. As a deep Bayesian generative network, the PGBN model obtains a more structured multi-layer feature representation from the complex SAR image data using the high nonlinearity of the Gamma distribution, and the multi-layer feature representation effectively improves SAR image target recognition performance. In order to obtain a higher recognition rate and efficiency of training, this paper further proposes a method for classifying PGBN model based on the Naive Bayes rule. The experimental results about MSTAR dataset show that the feature extracted by this new method has better structure information, and it has better performance for SAR image target recognition.
- SAR image,
- Feature extraction,
- Poisson Gamma Belief Network (PGBN) model

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References(26)

References

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