Feature Enhancement of Interferometric Synthetic Aperture Radar Image Formation Using Sparse Bayesian Learning in Joint Sparsity Approach

Hou Yuxing; Xu Gang

doi:10.12000/JR18100

Volume 7 Issue 6

Feb. 2019

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Article Navigation > Journal of Radars > 2018 > 7(6): 750-757

Hou Yuxing, Xu Gang. Feature Enhancement of Interferometric Synthetic Aperture Radar Image Formation Using Sparse Bayesian Learning in Joint Sparsity Approach[J]. Journal of Radars, 2018, 7(6): 750-757. doi: 10.12000/JR18100

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Hou Yuxing, Xu Gang. Feature Enhancement of Interferometric Synthetic Aperture Radar Image Formation Using Sparse Bayesian Learning in Joint Sparsity Approach[J]. Journal of Radars, 2018, 7(6): 750-757. doi: 10.12000/JR18100

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Feature Enhancement of Interferometric Synthetic Aperture Radar Image Formation Using Sparse Bayesian Learning in Joint Sparsity Approach

DOI: 10.12000/JR18100

Hou Yuxing^1
,,
Xu Gang^{2
,
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①.
Shaanxi Huanghe Group Co., LTD, Xi’an 710043, China
②.
State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China (61701106), The Natural Science Foundation of Jiangsu Province (BK20170698), The Innovative Talent Promotion Program of Shaanxi Province-Youth Science and Technology New Star Project (S2019-ZC-XXXM-0035)

Received Date: 2018-11-26
Rev Recd Date: 2018-12-18

Available Online: 2019-01-23

Publish Date: 2018-12-28

Abstract

Abstract

A novel sparse Bayesian learning approach with a joint sparsity model is proposed for Interferometric Synthetic Aperture Radar (InSAR) image formation to realize the feature enhancements of interferometric phase denoising and speckle reduction. Using Bayesian rules, sparse image formation is achieved using a hierarchical statistical model. In particular, structured sparsity with joint channels is imposed on the InSAR images. During sparse imaging, an Expectation-Maximization (EM) method is employed for image formation and hyper-parameter estimation. Using joint sparsity statistics, the performance of the noise reduction on the magnitude and phase of InSAR images can be improved. Finally, experimental analysis is performed using simulated and measured data to confirm the effectiveness of the proposed algorithm.
- Interferometric Synthetic Aperture Radar (InSAR),
- Joint sparsity,
- Bayesian,
- Interferometric phase de-noising,
- Speckle reduction

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References(21)

References

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