A Novel Cluster-Analysis Algorithm Based on MAP Framework for Multi-baseline InSAR Height Reconstruction

Si Qi; Wang Yu; Deng Yunkai; Li Ning; Zhang Heng

doi:10.12000/JR17043

Volume 6 Issue 6

Dec. 2017

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Article Navigation > Journal of Radars > 2017 > 6(6): 640-652

Si Qi, Wang Yu, Deng Yunkai, Li Ning, Zhang Heng. A Novel Cluster-Analysis Algorithm Based on MAP Framework for Multi-baseline InSAR Height Reconstruction[J]. Journal of Radars, 2017, 6(6): 640-652. doi: 10.12000/JR17043

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Si Qi, Wang Yu, Deng Yunkai, Li Ning, Zhang Heng. A Novel Cluster-Analysis Algorithm Based on MAP Framework for Multi-baseline InSAR Height Reconstruction[J]. Journal of Radars, 2017, 6(6): 640-652. doi: 10.12000/JR17043

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A Novel Cluster-Analysis Algorithm Based on MAP Framework for Multi-baseline InSAR Height Reconstruction

DOI: 10.12000/JR17043

Si Qi^{1,2
,
,},
Wang Yu^{1,2
,},
Deng Yunkai^1
,,
Li Ning^1
,,
Zhang Heng^{1,2
,}

①.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
②.
University of Chinese Academy of Sciences, Beijing 100039, China

Funds: The National Natural Science Foundation of China (61422113), The National Ten Thousand Talent ProgramYoung Top Notch Talent Program, The Hundred Talents Program of the Chinese Academy of Sciences

Received Date: 2017-04-01
Rev Recd Date: 2017-04-25
Publish Date: 2017-12-28

Abstract

Abstract

The multi-baseline InSAR can effectively reduce the adverse effect caused by the abrupt change of the target and the large noise disturbance and can obtain the Digital Elevation Model (DEM) that is more accurate than the single baseline InSAR. Traditional multi-baseline height reconstruction algorithm based on Maximum Likelihood (ML) estimation is poorly reconstructed in the case of fewer channels, and the height reconstruction algorithm based on Maximum A Posteriori estimation (MAP) has a long runtime defect; to solve this problem, this study proposes the cluster analysis based on maximum a posteriori algorithm. This algorithm uses the ML estimation to obtain a rough DEM. Based on this result, the noise pixels in the neighborhood in each iteration process are determined by cluster analysis. Finally, through the calculation of posterior probability to complete the reconstruction, an optimized method is adopted to improve the accuracy. Experiments reveal that the algorithm retains the speed of the ML method as well as the high precision of the MAP estimation, thus maintaining accuracy and high operating efficiency.
- Digital Elevation Model (DEM),
- Multi-baseline,
- Maximum Likelihood (ML) estimation,
- Maximum A Posteriori (MAP) estimation,
- Cluster-Analysis (CA)

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References(21)

References

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