Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 105, Issue , 2015, Pages 61-78

  Vetrivel, Anand ^a   Gerke, Markus ^a   Kerle, Norman ^a   Vosselman, George ^a  

^a UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

Building delineation; Damage pattern; Gap classification; Image based 3D point cloud; Oblique images; Texture; UAV

Indexed keywords

BUILDINGS; CLASSIFICATION (OF INFORMATION); DATA ACQUISITION; DECISION TREES; RADIOMETRY; ROOFS; TEXTURES; UNMANNED AERIAL VEHICLES (UAV); UNMANNED VEHICLES;

3D POINT CLOUD; CLASSIFICATION METHODS; DAMAGE ASSESSMENTS; DAMAGE PATTERN; GENERALIZATION PERFORMANCE; HISTOGRAM OF GRADIENTS; OBLIQUE IMAGES; VERY HIGH RESOLUTION;

DAMAGE DETECTION;

AIRBORNE SENSING; BUILDING; CLASSIFICATION; DATA ASSIMILATION; DATA SET; GEOMETRY; RADIOMETER; RESOLUTION; THREE-DIMENSIONAL MODELING;

EID: 84927645205     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2015.03.016     Document Type: Article

References (46)

1. Alsadik B., Gerke M., Vosselman G. Visibility analysis of point cloud in close range photogrammetry. ISPRS Ann. Photogramm. Rem. Sens. Spatial Inform. Sci. 2014, II-5:9-16.
2. Arivazhagan S., Ganesan L., Priyal S.P. Texture classification using Gabor wavelets based rotation invariant features. Pattern Recogn. Lett. 2006, 27(16):1976-1982.
3. Axelsson P. DEM generation from laser scanner data using adaptive TIN models. Int. Arch. Photogramm. Rem. Sens. 2000, 33(part B4/1):111-118.
4. Belgiu M., Tomljenovic I., Lampoltshammer T.J., Blaschke T., Höfle B. Ontology-based classification of building types detected from airborne laser scanning data. Rem. Sens. 2014, 6(2):1347-1366.
5. Breiman L. Random forests. Mach. Learn. 2001, 45(1):5-32.
6. Chen W., Ao T., Li H., Yang W., Li X. An improved approach of information extraction for earthquake-damaged buildings using high-resolution imagery. J. Earthq. Tsunami 2011, 05(04):389-399.
7. Colomina I., Molina P. Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J. Photogramm. Remote Sens. 2014, 92:79-97.
8. Dell'Acqua F., Gamba P. Remote sensing and earthquake damage assessment: experiences, limits, and perspectives. Proc. IEEE 2012, 100(10):2876-2890.
9. Dong L., Shan J. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS J. Photogramm. Remote Sens. 2013, 84:85-99.
10. Ehrlich D., Guo H.D., Molch K., Ma J.W., Pesaresi M. Identifying damage caused by the 2008 Wenchuan earthquake from VHR remote sensing data. Int. J. Digit. Earth 2009, 2(4):309-326.
11. Foody G.M., Boyd D.S., Cutler M.E.J. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sens. Environ. 2003, 85(4):463-474.
12. Gerke M., Kerle N. Automatic structural seismic damage assessment with airborne oblique Pictometry imagery. Photogramm. Eng. Remote Sens. 2011, 77(9):885-898.
13. Gerke, M., Kerle, N., 2011b. Graph matching in 3D space for structural seismic damage assessment. In: IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 6-13 November, 2011, pp. 204-211.
14. Haghighat M., Zonouz S., Abdel-Mottaleb M. Identification using encrypted biometrics. Computer Analysis of Images and Patterns 2013, Springer, pp. 440-448.
15. Hinsbergh W.H.M.v., Rijsdijk M.J., Witteveen W. UAS for cadastral applications: testing suitability for boundary identification in urban areas. GIM Int. 2013, 27:20-25.
16. Hussain E., Ural S., Kim K., Fu C.S., Shan J. Building extraction and rubble mapping for city port-au-prince post-2010 earthquake with geoeye-1 imagery and lidar data. Photogramm. Eng. Remote Sens. 2011, 77(10):1011-1023.
17. Kerle N. Satellite-based damage mapping following the 2006 Indonesia earthquake-how accurate was it?. Int. J. Appl. Earth Obs. Geoinf. 2010, 12(6):466-476.
18. Kerle N., Hoffman R.R. Collaborative damage mapping for emergency response: the role of Cognitive Systems Engineering. Nat. Hazard Earth Syst. Sci. (NHESS): Open Access 2013, 13:97-113.
19. Khoshelham K., Oude Elberink S., Sudan X. Segment-based classification of damaged building roofs in aerial laser scanning data. IEEE Geosci. Remote Sens. Lett. 2013, 10(5):1258-1262.
20. Kobayashi T., Hidaka A., Kurita T. Selection of histograms of oriented gradients features for pedestrian detection. Neural Information Processing 2008, 598-607. Springer, Berlin Heidelberg.
21. Lafarge F., Mallet C. Creating large-scale city models from 3D-point clouds: a robust approach with hybrid representation. Int. J. Comput. Vision 2012, 99(1):69-85.
22. Leberl F., Irschara A., Pock T., Meixner P., Gruber M., Scholz S., Wiechert A. Point clouds: lidar versus 3D vision. Photogramm. Eng. Remote Sens. 2010, 76(10):1123-1134.
23. Li, L., Zhang, B., Wu, Y., 2012. Fusing spectral and texture information for collapsed buildings detection in airborne image. In: IEEE Geoscience and Remote Sensing Symposium (IGARSS), 22-27 July, 2012, pp. 186-189.
24. Ma, J., Qin, S., 2012. Automatic depicting algorithm of earthquake collapsed buildings with airborne high resolution image. In: IEEE Geoscience and Remote Sensing Symposium (IGARSS), 22-27 July, 2012, pp. 939-942.
25. Minetto R., Thome N., Cord M., Leite N.J., Stolfi J. T-HOG: an effective gradient-based descriptor for single line text regions. Pattern Recogn. 2013, 46(3):1078-1090.
26. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans. Syst., Man Cybern. 1979, 9(1):62-66.
27. Oude Elberink S., Shoko M., Fathi S.A., Rutzinger M. Detection of collapsed buildings by classifying segmented airborne laser scanner data. Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci. 2011, XXXVIII:307-312.
28. Plank S. Rapid damage assessment by means of multi-temporal SAR-a comprehensive review and outlook to Sentinel-1. Remote Sens. 2014, 6(6):4870-4906.
29. Rabbani T., van den Heuvel F., Vosselman G. Segmentation of point clouds using smoothness constraint. Int. Arch. Photogramm., Remote Sens. Spatial Inform. Sci. 2006, 36(5):248-253.
30. Radhika S., Tamura Y., Matsui M. Use of post-storm images for automated tornado-borne debris path identification using texture-wavelet analysis. J. Wind Eng. Ind. Aerodyn. 2012, 107:202-213.
31. Rapidlasso, 2013. Homepage lastools (accessed 15.06.14). http://lastools.org.
32. Rathje E.M., Crawford M., Woo K., Neuenschwander A. Damage patterns from satellite images of the 2003 Bam, Iran, earthquake. Earthq. Spectra 2005, 21(S1):295-307.
33. Rehor M., Vögtle T. Improvement of building damage detection and classification based on laser scanning data by integrating spectral information. Int. Arch. Photogramm. Remote Sens. (IAPRS) 2008, 37(B7):1599-1605.
34. Rottensteiner F., Sohn G., Gerke M., Wegner J.D., Breitkopf U., Jung J. Results of the ISPRS benchmark on urban object detection and 3D building reconstruction. ISPRS J. Photogramm. Remote Sens. 2014, 93:256-271.
35. Ruiz, L., Fdez-Sarría, A., Recio, J., 2004. Texture feature extraction for classification of remote sensing data using wavelet decomposition: a comparative study. In: 20th ISPRS Congress, vol. 35(part B), pp. 1109-1114.
36. Rupnik E., Nex F., Remondino F. Oblique multi-camera systems - orientation and dense matching issues. ISPRS Arch. Photogramm., Remote Sens. Spatial Inform. Sci. 2014, 50(3/W1):107-114.
37. Salas Y.S., Bermudez D.V., Peña A.M.L., Gomez D.G., Gevers T. Improving HOG with image segmentation: application to human detection. Advanced Concepts for Intelligent Vision Systems 2012, Springer, pp. 178-189.
38. Schölkopf B., Smola A.J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) 2001, MIT Press, Cambridge, MA.
39. Shen, Y., Wu, L., Wang, Z., 2010. Identification of inclined buildings from aerial LIDAR data for disaster management. In: 18th IEEE International Conference on Geoinformatics, Beijing, China, pp. 1-5.
40. Sun S., Salvaggio C. Aerial 3D building detection and modeling from airborne LiDAR point clouds. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 2013, 6(3):1440-1449.
41. Trinder J., Salah M. Aerial images and lidar data fusion for disaster change detection. ISPRS Ann. Photogramm. Remote Sens. Spatial Inform. Sci. 2012, I-4:227-232.
42. Voigt S., Kemper T., Riedlinger T., Kiefl R., Scholte K., Mehl H. Satellite image analysis for disaster and crisis-management support. IEEE Trans. Geosci. Remote Sens. 2007, 45(6):1520-1528.
43. Vosselman G. Automated planimetric quality control in high accuracy airborne laser scanning surveys. ISPRS J. Photogramm. Remote Sens. 2012, 74:90-100.
44. Wikipedia, 2015 (accessed 06.02.15). http://simple.wikipedia.org/wiki/2011_Christchurch_earthquake#mediaviewer/File:Piko_building_corner_Barbadoes_Kilmore.jpg.
45. Xiong X., Adan A., Akinci B., Huber D. Automatic creation of semantically rich 3D building models from laser scanner data. Automat. Constr. 2013, 31:325-337.
46. Yamazaki F., Matsuoka M. Remote sensing technologies in post-disaster damage assessment. J. Earthq. Tsunami 2007, 1(03):193-210.