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

ntv.org.np

8.5 thousand human losses from the earthquake in Nepal on 25th of April 2015 (Chaulagain et al., 2016)

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)

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

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.

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.

Hollin Hill landslide, BGS observatory site

Hollin Hill landslide, BGS observatory site

▪ Panasonic:0.03 m GSD0.06 m DEM

▪ Sony:0.02 m GSD0.04 m DEM

Hollin Hill landslide, BGS observatory site

Methodology

Morphology-based co-Registration

(MBR)

Structure-from-Motion (SfM) Mutli-View Stereo (MVS) image matching pipeline

geo.tuwien.ac.at/opals

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

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

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).

Op

en

ne

ss-H

ollin

Hill

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.

▪ 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

▪ 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

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.

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

maria-valasia.peppa@ncl.ac.uk