Detecting Ground Deformation in the Built Environment using Sparse Satellite InSAR data with a Convolutional Neural Network

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1109/TGRS.2020.3018315.

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Authors

Nantheera Anantrasirichai , Juliet Biggs, Krisztina Kelevitz, Zahra Sadeghi, Tim J. Wright, James Thompson, Alin Achim, David Bull

Abstract

The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of non-expert stakeholders a challenge. Here we explore the applicability of deep learning approaches by adapting a pre-trained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the UK where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides and tunnelling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exists to construct a balanced training data set, and the deformation signals are slower and more localised than in previous applications. We propose three enhancement methods to tackle these problems: i) spatial interpolation with modified matrix completion, ii) a synthetic training dataset based on the characteristics of the real UK velocity map, and iii) enhanced over-wrapping techniques. Using velocity maps spanning 2015-2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.

DOI

https://doi.org/10.31223/osf.io/pw2gs

Subjects

Earth Sciences, Electrical and Computer Engineering, Engineering, Other Earth Sciences, Physical Sciences and Mathematics, Signal Processing

Keywords

machine learning, InSAR, Earth Observation, convolutional neural network, ground deformation, matrix completion

Dates

Published: 2020-05-13 19:15

Last Updated: 2020-09-02 13:43

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License

GNU Lesser General Public License (LGPL) 2.1

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