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Automated co-registration and calibration in SfM photogrammetry for landslide change detection

Lookup NU author(s): Dr Maria-Valasia Peppa, Professor Jon Mills, Professor Philip Moore

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by John Wiley and Sons Ltd, 2019.

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Abstract

Landslides represent hazardous phenomena, often with significant implications. Monitoring landslides with time‐series surface observations can indicate surface failure. Unmanned aerial vehicles (UAVs) employing compact digital cameras, in conjunction with Structure‐from‐Motion (SfM) and Multi‐View Stereo (MVS) image processing approaches, have become commonplace in the geoscience research community. These methods offer relatively low‐cost, flexible solutions for many geomorphological monitoring applications. However, conventionally ground control points (GCPs) are required for registration purposes, the provision of which is often expensive, difficult or even impracticable in hazardous and inaccessible terrain.In an attempt to overcome the reliance on GCPs, this paper reports research that has developed a morphology‐based strategy to co‐register multi‐temporal UAV‐derived products. It applies the attribute of curvature in combination with the scale‐invariant feature transform algorithm, to generate time‐invariant curvature features, which serve as pseudo GCPs. Openness, a surface morphological digital elevation model derivative, is applied to identify relatively stable ground regions from which pseudo GCPs are selected. A sensitivity threshold quantifies the minimum detectable change alongside unresolved biases and misalignment errors. The approach is evaluated at two study sites in the UK, firstly at Sandford with artificially induced surface change and secondly at an active landslide at Hollin Hill, with multi‐epoch SfM‐MVS products derived from a consumer‐grade UAV. Elevation changes and annual displacement rates at dm‐level are estimated, with optimal results achieved over winter periods. The morphology‐based co‐registration strategy resulted in relative error ratios (i.e. mean error divided by average flying height) in the range of 1:800‐2500, comparable to those reported by similar studies conducted with UAVs augmented with real time kinematic (RTK)‐Global Navigation Satellite Systems. Analysis demonstrates the potential of the morphology‐based strategy for a semi‐automatic, and practical co‐registration approach to quantify surface motion. This can ultimately complement geotechnical and geophysical investigations and support the understanding of landslide behaviour, model prediction and construction of measures for mitigating risks.


Publication metadata

Author(s): Peppa MV, Mills JP, Moore P, Miller PE, Chambers JE

Publication type: Article

Publication status: Published

Journal: Earth Surface Processes and Landforms

Year: 2019

Volume: 44

Issue: 1

Pages: 287-303

Print publication date: 01/01/2019

Online publication date: 06/09/2018

Acceptance date: 06/09/2018

Date deposited: 13/09/2018

ISSN (print): 0197-9337

ISSN (electronic): 1096-9837

Publisher: John Wiley and Sons Ltd

URL: https://doi.org/10.1002/esp.4502

DOI: 10.1002/esp.4502


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