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. Preprints2016, 2016080055. https://doi.org/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. https://doi.org/10.20944/preprints201608.0055.v1
Li, Q.; Gong, L.; Zhang, J. Seismic Damage Recognition Based on Watershed Segmentation of SAR Image Texture Features. Preprints2016, 2016080055. https://doi.org/10.20944/preprints201608.0055.v1
APA Style
Li, Q., Gong, L., & Zhang, J. (2016). Seismic Damage Recognition Based on Watershed Segmentation of SAR Image Texture Features. Preprints. https://doi.org/10.20944/preprints201608.0055.v1
Chicago/Turabian Style
Li, Q., Lixia Gong and Jingfa Zhang. 2016 "Seismic Damage Recognition Based on Watershed Segmentation of SAR Image Texture Features" Preprints. https://doi.org/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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.