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Automatic Classification of Collapsed Buildings using Stereo Aerial Images

by Mehdi Rezaeian
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 21
Year of Publication: 2012
Authors: Mehdi Rezaeian
10.5120/7069-9818

Mehdi Rezaeian . Automatic Classification of Collapsed Buildings using Stereo Aerial Images. International Journal of Computer Applications. 46, 21 ( May 2012), 35-42. DOI=10.5120/7069-9818

@article{ 10.5120/7069-9818,
author = { Mehdi Rezaeian },
title = { Automatic Classification of Collapsed Buildings using Stereo Aerial Images },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 21 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 35-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number21/7069-9818/ },
doi = { 10.5120/7069-9818 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:40:40.321506+05:30
%A Mehdi Rezaeian
%T Automatic Classification of Collapsed Buildings using Stereo Aerial Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 21
%P 35-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

After an earthquake, the image-based interpretation methods are powerful tools for detection and classification of damaged buildings. A method based on two kinds of image-extracted features comparing stereo pairs of aerial images before and after an earthquake is presented. Comparing pre- and post event DSMs - generated from stereo images - could be a solution for detecting the extent of demolished areas of buildings. However such DSMs are not sufficiently accurate due to image matching problems. We propose "Regularity indices" to describe the appearance of the building as regular or irregular. Regularity indices were defined by taking account of lines composition with regards to building footprint. In addition, a normalized value of average differences between DSMs (within each building polygon) is added into the classification procedures. Three kinds of classification methods: k-NN, naive Bayes and support vector machine (SVM) are used and compared. Experiments are performed on two datasets of the Kobe and Bam earthquakes including vast varieties of real collapsed buildings. The numerical results achieved for our datasets are very promising to detect and classify collapsed buildings automatically.

References
  1. Turker, M. and San, B. T. , 2004. Detection of collapsed buildings caused by the 1999 Izmit, Turkey earthquake through digital analysis of post-event aerial photographs. Int. Journal of Remote Sensing, 25 (21), pp. 4701–4714.
  2. Vu, T. T. , Matsuoka, M. , Yamazaki, F. , 2004. Shadow Analysis in Assisting Damage Detection Due to Earthquakes from Quickbird Imagery. International archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 35 (7), pp. 607-610.
  3. Turker, M. , Sumer, E. , 2008. Building-based Damage Detection due to Earthquake using the Watershed Segmentation of Post-event Aerial Images. International Journal of Remote Sensing, Vol. 29, No. 11, pp. 3073-3089.
  4. Li, P. 2011. Urban building damage detection from very high-resolution imagery by One-Class SVM and shadow information. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1409 – 1412.
  5. Sumer, E. , Turker, M. , 2005. Building Damage Detection from Post-Earthquake Aerial Imagery Using Building Grey-Value and Gradient Orientation Analyses. Proceedings of 2nd International Conference on Recent Advances in Space Technologies (RAST'05), Istanbul, Turkey, pp. 577-582.
  6. Rehor, M. , Vögtle, T. , 2008. Improvement of building damage detection and classification based on laser scanning data by integrating spectral information. International Archive of Photogrammetry and Remote Sensing (IAPRS), 37 (B7), pp. 1599-1605.
  7. Rehor, M. and Bähr H. -P. 2007. Detection and analysis of building damage caused by earthquakes using Laser scanning data. International Symposium on Strong Vrancea Earthquakes and Risk Mitigation, Bucharest, Romania, pp. 457-47.
  8. Rezaeian, M. and Gruen, A. 2007. Automatic classification of collapsed buildings using object and image space features. In: J. Li, S. Zlatanova and A. Fabbri (eds. ), Geomatics solutions for Disaster Management. Springer, pp. 135-148.
  9. Turker, M and Cetinkaya, B. 2005. Automatic detection of earthquake damaged buildings using DEMs created from pre- and post-earthquake stereo aerial photographs. International Journal of Remote Sensing, 26(4), pp. 823–832.
  10. Rezaeian, M. and Grün, A. , 2011. Comparative study of k-NN, naive Bayes and SVM methods for building collapse detection using image features, The 32nd Asian Conference on Remote sensing (ACRS).
  11. Shirzaei, M. , Mansouri, B. , Shinozuka, M. , 2006. Multiresolution Analysis of Satellite Optical Images for Damage Detection using Wavelet Transform. 4th International Workshop on Remote Sensing for Disaster Response, 25-26th, Cambridge, UK.
  12. Sertel, E. , Kaya, S. , Curran, P. J. , 2007. Use of Semivariograms to identify Earthquake Damage in an Urban Area. In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, Issue 6, pp. 1590-1594.
  13. Rezaeian, M. and Grün, A. , 2011. Automatic 3D building extraction from aerial and space images for earthquake risk management. Journal of Georisk (Taylor & Francis): Assessment and Management of Risk for Engineered Systems and Geohazards, 5(1), pp 77-96.
  14. Platt, J. C. , Cristianini, N. , Shawe-Taylor, J. 2000. Large Margin DAGs for multiclass classification. Advances in Neural Network Information Processing Systems, 12, pp. 547-553.
  15. Gonzalez, R. C. and Woods, R. E. 2008. Digital Image Processing - Third Edition. Prentice-Hall, Inc. , New Jersey.
Index Terms

Computer Science
Information Sciences

Keywords

Supervised Classification Collapse Detection Earthquake

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