Improved Maximum Likelihood Estimation for Optimal Phase History Retrieval of Distributed Scatterers in InSAR Stacks
Distributed scatterer (DS) decorrelation poses a challenge to multibaseline SAR interferometry. To overcome this challenge, the SqueeSAR retrieves an optimal phase time-series using a maximum likelihood estimation (MLE) method, which has been commonly used due to its remarkable effect. Unfortunately...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8937738/ |
| _version_ | 1818412586961469440 |
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| author | Changjun Zhao Zhen Li Ping Zhang Bangsen Tian Shuo Gao |
| author_facet | Changjun Zhao Zhen Li Ping Zhang Bangsen Tian Shuo Gao |
| author_sort | Changjun Zhao |
| collection | DOAJ |
| description | Distributed scatterer (DS) decorrelation poses a challenge to multibaseline SAR interferometry. To overcome this challenge, the SqueeSAR retrieves an optimal phase time-series using a maximum likelihood estimation (MLE) method, which has been commonly used due to its remarkable effect. Unfortunately, however, the MLE's performance is compromised for various reasons, such as inaccurate statistically homogeneous pixels (SHPs) and the bias in the estimator used. In this paper, we present an approach aiming to improve the MLE's performance. The proposed approach includes the employment of the Kullback-Leibler divergence to realize more accurate SHP selection and the use of the second kind statistical estimator to mitigate the coherence bias. The performance of the conventional MLE is significantly improved by the proposed approach, making it close to its optimal performance. The experimental results on both simulated and real TerraSAR-X data demonstrate the improvements of the proposed approach with respect to the conventional MLE. |
| first_indexed | 2024-12-14T10:49:40Z |
| format | Article |
| id | doaj.art-832e4179771a426aabb2983cd42887fd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:49:40Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-832e4179771a426aabb2983cd42887fd2022-12-21T23:05:18ZengIEEEIEEE Access2169-35362019-01-01718631918632710.1109/ACCESS.2019.29611548937738Improved Maximum Likelihood Estimation for Optimal Phase History Retrieval of Distributed Scatterers in InSAR StacksChangjun Zhao0https://orcid.org/0000-0002-8981-9009Zhen Li1https://orcid.org/0000-0003-3491-0697Ping Zhang2https://orcid.org/0000-0002-8401-4818Bangsen Tian3https://orcid.org/0000-0001-9449-0941Shuo Gao4https://orcid.org/0000-0002-1824-5028Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaDistributed scatterer (DS) decorrelation poses a challenge to multibaseline SAR interferometry. To overcome this challenge, the SqueeSAR retrieves an optimal phase time-series using a maximum likelihood estimation (MLE) method, which has been commonly used due to its remarkable effect. Unfortunately, however, the MLE's performance is compromised for various reasons, such as inaccurate statistically homogeneous pixels (SHPs) and the bias in the estimator used. In this paper, we present an approach aiming to improve the MLE's performance. The proposed approach includes the employment of the Kullback-Leibler divergence to realize more accurate SHP selection and the use of the second kind statistical estimator to mitigate the coherence bias. The performance of the conventional MLE is significantly improved by the proposed approach, making it close to its optimal performance. The experimental results on both simulated and real TerraSAR-X data demonstrate the improvements of the proposed approach with respect to the conventional MLE.https://ieeexplore.ieee.org/document/8937738/Differential interferometric synthetic aperture radar (DInSAR)statistically homogeneous pixels (SHPs)distributed scatterer (DS)maximum likelihood estimation (MLE) |
| spellingShingle | Changjun Zhao Zhen Li Ping Zhang Bangsen Tian Shuo Gao Improved Maximum Likelihood Estimation for Optimal Phase History Retrieval of Distributed Scatterers in InSAR Stacks IEEE Access Differential interferometric synthetic aperture radar (DInSAR) statistically homogeneous pixels (SHPs) distributed scatterer (DS) maximum likelihood estimation (MLE) |
| title | Improved Maximum Likelihood Estimation for Optimal Phase History Retrieval of Distributed Scatterers in InSAR Stacks |
| title_full | Improved Maximum Likelihood Estimation for Optimal Phase History Retrieval of Distributed Scatterers in InSAR Stacks |
| title_fullStr | Improved Maximum Likelihood Estimation for Optimal Phase History Retrieval of Distributed Scatterers in InSAR Stacks |
| title_full_unstemmed | Improved Maximum Likelihood Estimation for Optimal Phase History Retrieval of Distributed Scatterers in InSAR Stacks |
| title_short | Improved Maximum Likelihood Estimation for Optimal Phase History Retrieval of Distributed Scatterers in InSAR Stacks |
| title_sort | improved maximum likelihood estimation for optimal phase history retrieval of distributed scatterers in insar stacks |
| topic | Differential interferometric synthetic aperture radar (DInSAR) statistically homogeneous pixels (SHPs) distributed scatterer (DS) maximum likelihood estimation (MLE) |
| url | https://ieeexplore.ieee.org/document/8937738/ |
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