Automated processing of high resolution airborne images for earthquake damage assessment

Transcription

1 Automated processing of high resolution airborne images for earthquake damage assessment Ewelina RUPNIK 3D Optical Metrology (3DOM) Bruno Kessler Foundation (FBK) Trento, Italy

2 OUTLINE Introduction Airborne imaging platforms Photogrammetric processing Earthquake damage assessment Methods First results Conclusion and future work 2

3 INTRODUCTION Optical imagery for earthquake damage assessment Different temporal and spatial resolutions (airborne, spaceborne) Pre- and/or post-event data Use of ancillary data, fusion (LiDAR, maps, GIS) 2D and/or 3D approaches Nadir and/or oblique imagery 3

4 INTRODUCTION Airborne imaging platforms for earthquake damage assessment Traditional aerial flights e.g. [Li et al., 2014; Gerke and Kerle, 2011;] San Francisco Earthquake, 1906 Wenchuan earthquake 2010, China Haiti earthquake 2010, Dominican Rep. images from kite-borne camera nadir views nadir and oblique views UAV e.g. [Chou et al., 2010; Huber 2010; Adams and Friedland, 2011] Hurricane Katrina 2006, Mississipi Gulf, USA L Aquila earthquake 2010, Italy Haiti earthquake 2010, Dominican Rep. Japan earthquakes

5 INTRODUCTION Airborne imaging platforms for earthquake damage assessment Traditional aerial flights e.g. [Li et al., 2014; Gerke and Kerle, 2011;] San Francisco Earthquake, 1906 Wenchuan earthquake 2010, China Haiti earthquake 2010, Dominican Rep. images from kite-borne camera nadir views nadir and oblique views UAV e.g. [Chou et al., 2010; Huber 2010; Adams and Friedland, 2011] Hurricane Katrina 2006, Mississipi Gulf, USA L Aquila earthquake 2010, Italy Haiti earthquake 2010, Dominican Rep. Japan earthquakes 2011 PHOTO-INTERPRETATION apt for non-experts COMPLETENESS 2.5 versus 3D COST & ACESSIBILITY systematic acquisition, growing market: Pictometry, MIDAS, DigiCAM, UltraCAM Osprey, Leica RCD30 5

6 OBJECTIVE Earthquake induced building damage area, grade of damage, type of damage European Macroseismic Scale 1998 (EMS98) Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 6

7 OBJECTIVE Earthquake induced building damage area, grade of damage, type of damage European Macroseismic Scale 1998 (EMS98) Grade 3 5 (EMS98) Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 7

8 OBJECTIVE Earthquake induced building damage area, grade of damage, type of damage European Macroseismic Scale 1998 (EMS98) Grade 3 5 (EMS98) Individual building, Roof damage, and Façade structural damage detection Building Damage Assessment Map Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 8

9 OBJECTIVE Earthquake induced building damage area, grade of damage, type of damage European Macroseismic Scale 1998 (EMS98) Grade 3 5 (EMS98) Individual building, Roof damage, and Façade structural damage detection Building Damage Assessment Map Evaluate with: Spectral information in nadir and oblique imagery, and Geometric information in photogrammetric DSM Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 9

10 Photogrammetric processing Input imagery Manned/unmanned, nadir/oblique Different resolution Different imaging geometry Consequences/challenges Varying scale within images Less similarity between images Occlusions Complex image overlaps AUTOMATED PROCESSING MORE DIFFICULT Diff. imaging geometry Diff. resolution 10

11 OBLIQUE NADIR Photogrammetric processing Image orientation [Rupnik et al., 2013] Tie point extraction Approximate orientation Direct methods Orientation built step by step Careful concatenation necessary Refined bundle adjustment Image dense matching [Nex and Remondino, 2012] Dense but 1 object point per pixel Noisy Shadows and occlusion True orthophoto generation 11

12 Earthquake damage assessment methods Classification 1/3 Given Spectral Geometric information Photogrammetric DSM True orthophoto implicit Goal Thematic information Ground Vegetation Building Roof Facade explicit 12

13 Earthquake damage assessment methods Classification 1/3 13

14 Earthquake damage assessment methods 14

15 Earthquake damage assessment methods Classification 2/3 Sequential approach BUILDING LOCALIZATION NADIR DAMAGE LOCALIZATION NADIR FACADE DAMAGE ASSESSMENT (TO DO) OBLIQUE 15

16 Earthquake damage assessment methods Classification 2/3 Sequential approach BUILDING LOCALIZATION NADIR BUILDING VEGETATION HIGH VEGETATION LOW GROUND 16

17 Earthquake damage assessment methods Classification 2/3 Sequential approach BUILDING LOCALIZATION NADIR DAMAGE LOCALIZATION NADIR BUILDING INTACT BUILDING DAMAGED VEGETATION HIGH VEGETATION LOW GROUND XZ = DSM DEM XSV = SLOPE VARIABILITY OVER A CONSTRAINED REGION FEATURES X 17

18 Earthquake damage assessment methods Classification 2/3 Sequential approach BUILDING LOCALIZATION NADIR DAMAGE LOCALIZATION NADIR FACADE DAMAGE ASSESSMENT (TO DO) OBLIQUE verification of harmed entities structural damage assessment by pointcloud modelling and 2D image processing 18

19 Earthquake damage assessment results Classification 3/3 Dataset Where San Felice, Italy When May 2012 Magnitude 6.0 MMS Nadir post UltraCAM XP, ~5cm GSD, 80/60 overlap Oblique post Midas 5, ~10cm GSD (nadir), 70/50 overlap 19

20 Earthquake damage assessment results Classification 3/3 Area 1 B DAMAGED B INTACT VEGETATION GROUND 20

21 Earthquake damage assessment results Classification 3/3 Area 2 B DAMAGED B INTACT VEGETATION GROUND 21

22 Conclusions and future work A methodology was presented that uses - spectral and - geometric information derived from optical images in order to classify buildings as intact or damaged in an unsupervised manner; Adopted features (PNDVI, Z, SLOPE VARIABILITY) with their normaliztion factors are discriminative yet occasionally might prove insufficient thus more feature should be tested and added to the model (possibly through learning); EMS Grade 4-5 are detectable with the given approach Further novelty of the approach is in the unsupervised approach and the use of photogrammetric DSM; Despite the developments in digital photogrammetry, the DSM produced in an automated manner can be noisy because of occlusion and shadowing effects; Appropriate filtering techniques can largely mitigate the blunders in DSM Future works include the use of oblique images and the derived pointclouds to verify the potentially harmed entities as well as evaluate EMS Grade 3 damages 22

23 Bibliography Adams, Stuart M., and Carol J. Friedland, A survey of unmanned aerial vehicle (UAV) usage for imagery collection in disaster research and management. 9th International Workshop on Remote Sensing for Disaster Response. Boykov, Y., Veksler, O., Zabih, R., Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (11), Chou, T.-Y., M.-L. Yeh, et al., Disaster Monitoring and Management by the Unmanned Aerial Vehicle Technology. ISPRS Technical Commission VII Symposium. W. Wagner and B. Szekely. Vienna, Austria, IAPRS. XXXVIII: 6. Dong, L., and Shan J., A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing 84, Gerke, M., Kerle, N., Automatic structural seismic damage assessment with airborne oblique pictometry imagery. Photogrammetric Engineering and Remote Sensing 77 (9), Huber, M Evergreen supports UAV team mapping Haitian Relief. Aviation International News. March Li, Z., Jiao, Q., Liu, L., Tang, H., & Liu, T., Monitoring Geologic Hazards and Vegetation Recovery in the Wenchuan Earthquake Region Using Aerial Photography. ISPRS International Journal of Geo-Information 3.1, Nex, F., Remondino, F., 2012: Automatic roof outlines reconstruction from photogrammetric DSM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. I(3), Rupnik, E., Nex, F., and Remondino, F., Automatic orientation of large blocks of oblique images. Int. Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 40(1/W1),