Preprint Article Version 1 NOT YET PEER-REVIEWED

Seismic Damage Recognition Based on Watershed Segmentation of SAR Image Texture Features

  1. Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
  2. Key Laboratory of Crustal Dynamics, Institute of Crustal Dynamics, China Earthquake Administration, Beijing 100085, China
Version 1 : Received: 5 August 2016 / Approved: 5 August 2016 / Online: 5 August 2016 (12:19:24 CEST)

How to cite: Li, Q.; Gong, L.; Zhang, J. Seismic Damage Recognition Based on Watershed Segmentation of SAR Image Texture Features. Preprints 2016, 2016080055 (doi: 10.20944/preprints201608.0055.v1). Li, Q.; Gong, L.; Zhang, J. Seismic Damage Recognition Based on Watershed Segmentation of SAR Image Texture Features. Preprints 2016, 2016080055 (doi: 10.20944/preprints201608.0055.v1).

Abstract

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

Subject Areas

seismic damage building; watershed segmentation; SAR; texture feature; change detection

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