Automated co registration in SfM photogrammetry for landslide … · 2018. 12. 16. · Automated...
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Automated co‐registration in SfM
photogrammetry for
landslide change detectiondoi: 10.1002/esp.4502
Topo@drone,
Σχολή Αγρονόμων Τοπογράφων ΜηχανικώνΕΜΠ, 30/11/2018
Μαρία Βαλασία Πέππα
Jon Mills, Philip Moore,
Jon Chambers, Pauline Miller
School of Engineering Newcastle University,
British Geological Survey, The James Hutton Institute, UK
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ntv.org.np
8.5 thousand human losses from the earthquake in Nepal on 25th of April 2015 (Chaulagain et al., 2016)
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Introduction
© BGS
❑ Σημαντική αύξηση στις κατολισθήσεις κατά τη διάρκεια του 2012 λόγω της αύξησης των ακραίων βροχοπτώσεων το ίδιο έτος(Uhlemann et al., 2015; Gariano and Guzzetti, 2016)
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Research motivation
Any type of UAV High-grade UAV Low-grade UAV
Planimetric accuracy few cm 40 cm 2-10 m
Vertical accuracy few cm 35 cm 2-30 m
Indirect
georeferencing
(IG) (with GCPs)
Direct georeferencing (DG)
UAV cam. positions
hours
minutes
IG
High-grade
RTK-UAVLow-grade
centimetres metres
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PhD Aim/Contribution
Aim:
The potential of a mini fixed-wing UAV to a) register multi-temporal imagery (ταύτιση εικόνων πολλαπλών χρονικών περιόδων) b) provide 3D landslide time-series (3Δ επιφανειακής μετακίνησης)in the absence of physically established GCPs (χωρίς σημεία ελέγχου-φωτοσταθερά)
PhD Contribution:
This study introduces a semi-automatic workflow to generate “pseudo control” over relatively “stable” terrain for the effective co-registration of time-series DEMs derived from a consumer-grade, fixed-wing mini UAV and SfM-MVS pipeline.
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Hollin Hill landslide, BGS observatory site
Shaded relief together with two flight trajectories.
An active slow moving earth-slide, earth-flow landslide with an average 2 m/yr movement rate (Chambers et al., 2011; Uhlemann et al. (2017).
(a) Quest-300 UAV with Panasonic DMC-LX5. (b) Payload setup inside the main UAV body. (c) AutoCAD 3D model and (d) 3D printed version of a camera case to hold the Sony A6000 camera.
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Hollin Hill landslide, BGS observatory site
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Hollin Hill landslide, BGS observatory site
▪ Panasonic:0.03 m GSD0.06 m DEM
▪ Sony:0.02 m GSD0.04 m DEM
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Hollin Hill landslide, BGS observatory site
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Methodology
Morphology-based co-Registration
(MBR)
Structure-from-Motion (SfM) Mutli-View Stereo (MVS) image matching pipeline
geo.tuwien.ac.at/opals
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Hollin Hill landslide: Key-points as derived from the SIFT-RANSAC implementationwith (a) E0 (12/14) and (b) E4 (02/16) orthomosaics.
SIFT algorithm with optical orthomosaic
Wrong matches Good matches
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Hollin Hill landslide: Mean curvature grids of (a) E0 (12/14) and (b) E4 (02/16) epochs with their corresponding pseudo GCPs over stable terrain.
Morphology-based co-Registration analysis
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Stable terrain - Openness
• Identification of stable (smooth) and unstable (rough) terrain
• Due to underlying surface mechanisms, an active landslide area (i.e. failed terrain) has relatively rougher surface topography than a non-failing region.
Modified from McKean ad Roering (2004) and Hobson (1972)
(a) positive openness
L
nadirL
zenith
(b) negative openness
Positive (a) and negative (b) openness at a particular point of a DEM with L denoting the spatial limit. Extracted and modified from Yokoyama et al. (2002) and Chen et al. (2015).
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Op
en
ne
ss-H
ollin
Hill
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Results-Hollin Hill landslide
RMSEs and sensitivity (s1, s2) estimations of the GCP-based and MBR-GCP experiments.
❑ RMSEs and sensitivity (s1, s2) estimations of the GCP-based and MBR-GCP experiments.
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▪ RMSEs (Panasonic) 1.9-3.3 x GSD RMSEs (Sony) 1-1.6 x GSD
▪ 0.109 m optimal sensitivity
▪ 0.109-0.221 m sensitivity range with biases e.g. seasonal variationsunresolved DEM deformations unreliable pseudo GCPs etc.
✓ Elevation differences
✓ 2D displacement rate
✓ Volume change
Hollin Hill
landslide change
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▪ RMSEs (Panasonic) 1.9-3.3 x GSD RMSEs (Sony) 1-1.6 x GSD
▪ 0.109 m optimal sensitivity
▪ 0.109-0.221 m sensitivity range with biases e.g. seasonal variationsunresolved DEM deformations unreliable pseudo GCPs etc.
✓ Elevation differences
✓ 2D displacement rate
✓ Volume change
Hollin Hill
landslide change
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Conclusions
❑ A morphology-based co-registration (MBR) strategy aligns multi-temporal UAV and SfM-derived products for quantifying landslide information, without the usual reliance on ground control information, as a low-cost solution.
❑ It applies the openness roughness measure to identify stable surfaces and the SIFT algorithm with curvature grids to automatically extract correspondences in epoch-pairs, incorporating them into the SfM-photogrammetry.
❑ Relative error ratios (RMSE/flying height) from MBR results lie in the range 1:800-2500, are in good agreement to the ratios 1:1600-1900 reported in recent studies with RTK-UAVs (Gerke and Przybilla, 2016; Dall'Asta et al., 2017).
❑ A better outcome could be achieved mostly with periodic observations of a higher temporal frequency and over stable regions that are not adversely affected by vegetation changes.
❑ Further work over other landslide types is required.
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Ευχαριστώ πολύ
Peppa et al. 2018, Automated co-registration and calibration in SfMphotogrammetry for landslide change detection,
Earth Surface Processes and Landforms (ESPL), doi: 10.1002/esp.4502
UAV geomatics team