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Interferometric SAR Coherence Magnitude Estimation by Machine Learning

Adam, Nico Alexander (2023) Interferometric SAR Coherence Magnitude Estimation by Machine Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, pp. 3034-3044. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2023.3257047. ISSN 1939-1404.

[img] PDF - Postprint version (accepted manuscript)

Official URL: https://ieeexplore.ieee.org/document/10068755


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 (2 ≤ N < 30) 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.

Item URL in elib:https://elib.dlr.de/194396/
Document Type:Article
Title:Interferometric SAR Coherence Magnitude Estimation by Machine Learning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Adam, Nico AlexanderUNSPECIFIEDhttps://orcid.org/0000-0002-6053-0105UNSPECIFIED
Date:14 March 2023
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:pp. 3034-3044
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Coherence magnitude, degree of coherence, distributed scatterer in SqueeSAR or CESAR or phase linking, Gradient Boosted Trees, interferometric SAR (InSAR), Supervised Machine Learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - SAR methods
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Adam, Nico Alexander
Deposited On:20 Mar 2023 11:18
Last Modified:19 Oct 2023 15:05

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