Interferometric SAR Coherence Magnitude Estimation by Machine Learning
Current interferometric wide area ground motion services require the estimation of the coherence magnitude as accurately and computationally effectively as possible. However, a precise and at the same time computationally efficient method is missing. Therefore, the objective of this article is to im...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10068755/ |
| _version_ | 1797855390444027904 |
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| author | Nico Adam |
| author_facet | Nico Adam |
| author_sort | Nico Adam |
| collection | DOAJ |
| description | Current interferometric wide area ground motion services require the estimation of the coherence magnitude as accurately and computationally effectively as possible. However, a precise and at the same time computationally efficient method is missing. Therefore, the objective of this article is to improve the empirical Bayesian coherence magnitude estimation in terms of accuracy and computational cost. Precisely, this article proposes the interferometric coherence magnitude estimation by machine learning (ML). It results in a nonparametric and automated statistical inference. However, applying ML in this estimation context is not straightforward. The number and the domain of possible input processes is infinite and it is not possible to train all possible input signals. It is shown that the expected channel amplitudes and the expected interferometric phase cause redundancies in the input signals allowing to solve this issue. Similar to the empirical Bayesian methods, a single parameter for the maximum underlaying coherence is used to model the prior. However, no prior or any shape of prior probability is easy to implement within the ML framework. The article reports on the bias, standard deviation and RMSE of the developed estimators. It was found that ML estimators improve the coherence estimation RMSE from small samples (<inline-formula><tex-math notation="LaTeX">$2 \leq N < 30$</tex-math></inline-formula>) and for small underlaying coherence compared to the conventional and empirical Bayes estimators. The developed ML coherence magnitude estimators are suitable and recommended for operational InSAR systems. For the estimation, the ML model is extremely fast evaluated because no iteration, numeric integration or bootstrapping is needed. |
| first_indexed | 2024-04-09T20:21:51Z |
| format | Article |
| id | doaj.art-3aa2f9d7a6c84a8687b0a45c1c094f58 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-09T20:21:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-3aa2f9d7a6c84a8687b0a45c1c094f582023-03-30T23:00:10ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01163034304410.1109/JSTARS.2023.325704710068755Interferometric SAR Coherence Magnitude Estimation by Machine LearningNico Adam0https://orcid.org/0000-0002-6053-0105DLR-IMF, German Aerospace Center (DLR), Wessling, GermanyCurrent interferometric wide area ground motion services require the estimation of the coherence magnitude as accurately and computationally effectively as possible. However, a precise and at the same time computationally efficient method is missing. Therefore, the objective of this article is to improve the empirical Bayesian coherence magnitude estimation in terms of accuracy and computational cost. Precisely, this article proposes the interferometric coherence magnitude estimation by machine learning (ML). It results in a nonparametric and automated statistical inference. However, applying ML in this estimation context is not straightforward. The number and the domain of possible input processes is infinite and it is not possible to train all possible input signals. It is shown that the expected channel amplitudes and the expected interferometric phase cause redundancies in the input signals allowing to solve this issue. Similar to the empirical Bayesian methods, a single parameter for the maximum underlaying coherence is used to model the prior. However, no prior or any shape of prior probability is easy to implement within the ML framework. The article reports on the bias, standard deviation and RMSE of the developed estimators. It was found that ML estimators improve the coherence estimation RMSE from small samples (<inline-formula><tex-math notation="LaTeX">$2 \leq N < 30$</tex-math></inline-formula>) and for small underlaying coherence compared to the conventional and empirical Bayes estimators. The developed ML coherence magnitude estimators are suitable and recommended for operational InSAR systems. For the estimation, the ML model is extremely fast evaluated because no iteration, numeric integration or bootstrapping is needed.https://ieeexplore.ieee.org/document/10068755/Coherence magnitudegradient boosted treessupervised machine learning (ML)degree of coherencedistributed scatterer in SqueeSAR or CESAR or phase linkinginterferometric SAR (InSAR) |
| spellingShingle | Nico Adam Interferometric SAR Coherence Magnitude Estimation by Machine Learning IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Coherence magnitude gradient boosted trees supervised machine learning (ML) degree of coherence distributed scatterer in SqueeSAR or CESAR or phase linking interferometric SAR (InSAR) |
| title | Interferometric SAR Coherence Magnitude Estimation by Machine Learning |
| title_full | Interferometric SAR Coherence Magnitude Estimation by Machine Learning |
| title_fullStr | Interferometric SAR Coherence Magnitude Estimation by Machine Learning |
| title_full_unstemmed | Interferometric SAR Coherence Magnitude Estimation by Machine Learning |
| title_short | Interferometric SAR Coherence Magnitude Estimation by Machine Learning |
| title_sort | interferometric sar coherence magnitude estimation by machine learning |
| topic | Coherence magnitude gradient boosted trees supervised machine learning (ML) degree of coherence distributed scatterer in SqueeSAR or CESAR or phase linking interferometric SAR (InSAR) |
| url | https://ieeexplore.ieee.org/document/10068755/ |
| work_keys_str_mv | AT nicoadam interferometricsarcoherencemagnitudeestimationbymachinelearning |