Marine spill oil SAR images despeckling based on hidden Markov tree model in complex contourlet domain

WU Yiquan; SONG Yu; WU Shihua; ZHANG Yufei

doi:10.3969/j.issn.0253 4193.2013.02.018

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WU Yiquan, SONG Yu, WU Shihua, ZHANG Yufei. Marine spill oil SAR images despeckling based on hidden Markov tree model in complex contourlet domain[J]. Haiyang Xuebao, 2013, 35(2): 168-177. doi: 10.3969/j.issn.0253 4193.2013.02.018

Citation:

WU Yiquan, SONG Yu, WU Shihua, ZHANG Yufei. Marine spill oil SAR images despeckling based on hidden Markov tree model in complex contourlet domain[J]. Haiyang Xuebao, 2013, 35(2): 168-177. doi: 10.3969/j.issn.0253 4193.2013.02.018

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Marine spill oil SAR images despeckling based on hidden Markov tree model in complex contourlet domain

doi: 10.3969/j.issn.0253 4193.2013.02.018

1.
School of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;Key Laboratory of Marine Spill Oil Identification and Damage Assessment Technology, Qingdao 266033, China
2.
School of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received Date: 2011-11-16
Rev Recd Date: 2012-05-02

Abstract

Abstract

The presence of speckle noise in the marine spill oil SAR images seriously affects the follow-up image segmentation, feature extraction and classification. To suppress the speckle in the marine spill oil SAR images more effectively, a method of reducing the speckle noise in the marine spill oil SAR images based on the hidden Markov tree model in complex Contourlet transform domain is proposed in this paper.firstly, the observed image is taken the logarithm and the complex contourlet transform is performed. Then the hidden Markov tree model is adopted to a model the band pass directional subband coefficients between adjacent scales in complex contourlet domain. Moreover, the denoised coefficients are estimated according to Bayes minimum mean square error criterion. Finally, the inverse complex contourlet transform and the exponential transform are performed to obtain the despeckled image. A large number of experimental results show that, compared with four classical filtering methods such as Lee filter, Kuan filter, Frost filter and Gamma Map filter, and the methods based on the hidden Markov tree model in wavelet or contourlet transform domain, the proposed method in this paper has superior comprehensive performance according to subjective visual and objective quantitative evaluation. It is an effective preprocessing method of marine spill oil detection based on SAR remote sensing images.
- marine spill oil,
- SAR image,
- despeckling,
- complex contourlet transform,
- hidden markov tree model,
- bayesian estimation

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References(23)

References

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