PolSAR Ship Detection Method Based on Multiple Polarimetric Scattering Mechanisms

WEN Wei; CAO Xuefei; ZHANG Xuefeng; CHEN Bo; WANG Yinghua; LIU Hongwei

doi:10.11999/JEIT160204

Volume 39 Issue 1

Jan. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(1): 103-109

WEN Wei, CAO Xuefei, ZHANG Xuefeng, CHEN Bo, WANG Yinghua, LIU Hongwei. PolSAR Ship Detection Method Based on Multiple Polarimetric Scattering Mechanisms[J]. Journal of Electronics & Information Technology, 2017, 39(1): 103-109. doi: 10.11999/JEIT160204

Citation:

WEN Wei, CAO Xuefei, ZHANG Xuefeng, CHEN Bo, WANG Yinghua, LIU Hongwei. PolSAR Ship Detection Method Based on Multiple Polarimetric Scattering Mechanisms[J]. Journal of Electronics & Information Technology, 2017, 39(1): 103-109. doi: 10.11999/JEIT160204

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PolSAR Ship Detection Method Based on Multiple Polarimetric Scattering Mechanisms

doi: 10.11999/JEIT160204

Funds:

The National Science Fund for Distinguished Young Scholars (61525105), The National Natural Science Foundation of China (61201292, 61322103, 61372132), The Program for New Century Excellent Talents in University (FANEDD-201156), The Natural Science Basic Research Plan in Shaanxi Province of China (2016JQ6048), The Aviation Science Fund (20142081009) and Key Laboratory Fund of RF Integrated Laboratory in Avionics System, Shanghai Aerospace Science and, Technology Innovation Fund (SAST2015009)

Received Date: 2016-03-03
Rev Recd Date: 2016-08-23
Publish Date: 2017-01-19

Abstract

Abstract

Considering the shortcoming of detection method based on polarimetric contrast enhanced with single polarimetric scattering mechanism, a PolSAR detection method based on multiple polarimetric mechanisms called Dirichlet Process mixture of Latent Variable SVM (DPLVSVM) is proposed. By assembling a set of local polarimetric detectors that based on single polarimetric scattering mechanism, a global multiple polarimetric scattering mechanisms detector is obtained. With a fully Bayes treatment, DPLVSVM learns the clustering and the local detectors jointly. Taking the advantage of Bayes nonparametric, DPLVSVM handles the model selection problem flexibly. Further, in order to reduce the redundancy of polarimetric feature and improve the model generalization, a model with feature selection, Sparsity-Promoting Dirichlet Process mixture of Latent Variable SVM (SPDPLVSVM), is proposed. Thanks to the conjugate property, the parameters in both of models can be inferred efficiently via the Gibbs sampler. Finally, the proposed models on RADARSAR-2 dataset is implemented to validate their effectiveness.
- Polarimetric SAR,
- Target detection,
- Dirichlet process mixture model,
- Bayes SVM,
- Feature selection

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References(31)

References

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