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Li Jinxiang, Zhao Shuo, Jin Hua, Li Yafang, Guo Yin. 2019: A method of combined texture features and morphology for building seismic damage information extractionbased on GF remote sensing images. Acta Seismologica Sinica, 41(5): 658-670. DOI: 10.11939/jass.20190014
Citation: Li Jinxiang, Zhao Shuo, Jin Hua, Li Yafang, Guo Yin. 2019: A method of combined texture features and morphology for building seismic damage information extractionbased on GF remote sensing images. Acta Seismologica Sinica, 41(5): 658-670. DOI: 10.11939/jass.20190014
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A method of combined texture features and morphology for building seismic damage information extractionbased on GF remote sensing images

  • Earthquake Agency of Xinjiang Uygur Autonomous Region，Ürümqi 830011，China

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  • Received Date: January 14, 2019
  • Revised Date: July 07, 2019
  • Available Online: October 14, 2019
  • Published Date: August 31, 2019
    Graphical Abstract
    • Abstract
  • It is of great significance to study the methods in the extraction of building seismic damage information based on high-resolution remote sensing images in China, which can improve the timeliness of seismic damage information acquisition. Taking an earthquake with MS5.5 occurred near Taxkorgan Tajik Autonomous County, Kashi Prefecture, Xinjiang Uygur Autonomous Region, China, on May 11, 2017, as an example, based on high-resolution remote sensing images before and after the earthquake, building information was extracted by the method of combined texture features and morphology. Building damage information in extremely disaster areas was extracted through change detection and analysis, and then compared with the results extracted by pixel-based and object-based methods. Finally, the accuracy was verified by visual interpretation results of unmanned aerial vehicle images after the earthquake. The results show that the accuracy and speed of data extraction can be greatly improved by reducing the scope of the studied area. Using gray level co-occurrence matrix, binarization, mathematical morphology and other methods we can extract building information from GF remote sensing images more effectively. Through the change detection and analysis of building extraction results before and after the earthquake, completely collapsed buildings can be effec-tively extracted. The overall accuracy of information extraction is 90.45%, which is 5.78% and 5.23% higher than that of pixel-based and object-based information extraction, respectively. The completely collapsed buildings information can provide decision-making basis for rapid determination of people buried places and deployment of rescue forces after earthquakes, and improve the timeliness of earthquake emergency rescue.
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