Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1–5, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1–5, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

EARTHQUAKE DAMAGE DETECTION USING SATELLITE IMAGES (CASE STUDY: SARPOL-ZAHAB EARTHQUAKE)

H. Aali1, A. Sharifi2, and A. Malian2 H. Aali et al.
  • 1Graduate Student of Remote Sensing, Shahid Rajaee Teacher Training University, Tehran, Iran
  • 2Department of Surveying Engineering Shahid Rajaee Teacher Training University, Tehran, Iran

Keywords: Earthquake, Sarpol-Zahab, Remote Sensing, Change Detection, Detection of Changes, Image Classification, Satellite Images

Abstract. After the earthquake in Sarpol-e Zahab city, many people were killed or wounded and many buildings were destroyed. After such a destructive event, it is of great interest to efficiently identify the magnitude and the extent of the damaged areas. Remote sensing is an excellent technology for this purpose. Usually, a higher success rate can be achieved when both pre and post-event data, especially multi-view data, are used. Whereas when only post-event data are available, the detection is usually limited to the block level unless VHR images of a resolution of 0.5 m or higher are involved.The available dataset consists of one Pleiades-1 satellite optical image (post-event), two Sentinel-2 satellite images (pre&post event).After classification of the sentinel images(pre&post event) and preparation change maps by means of SVM and the neural network classification methods, two change maps will be provided. Then, A reference change map is prepared with ROIs. For this purpose, on the Pleiades-1 image (after the earthquake), ROIs in two categories “change” and “no change” are defined. In the last step, using the confusion matrix, two change maps from the Sentinel image are compared to the reference image, and the results are analyzed. The producer’s accuracy for detecting the collapsed buildings in the SVM classification method was found to be 78.34% and for the neural network classification was found to be 72.43%. The results show that the change map of the pre- and post-earthquake medium-resolution satellite images such as Sentinel-2 can reveal the collapsed buildings caused by the earthquake successfully.