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Classification of Post-earthquake High Resolution Image Using Adaptive Dynamic Region Merging and Gravitational Self-Organizing Maps

  • Aizhu Zhang
  • Yanling Hao
  • Genyun Sun
  • Jinchang RenEmail author
  • Huimin Zhao
  • Sophia Zhao
  • Tariq S. Durrani
Chapter
Part of the Springer Natural Hazards book series (SPRINGERNAT)

Abstract

Post-earthquake high resolution image classification has opened up the possibility for rapid damage mapping, which is crucial for damage assessments and emergency rescue. However, the classification accuracy is challenged by the diversity of disaster types as well as the lack of uniform statistical characteristics in post-earthquake high resolution images. In this paper, combining adaptive dynamic region merging (ADRM) and gravitational self-organizing map (gSOM), we propose a novel object-based classification framework. This approach consists of two parts: the segmentation by ADRM and the classification by gSOM. The ADRM produces the homogeneous regions by integrating an adaptive spectral-texture descriptor with a dynamic merging strategy. The gSOM regards the regions as basic unit and characterized them explicitly by fractal texture to adapt to various disaster types. Subsequently, these regions are represented by neurons in a self-organizing map and clustered by adjacency gravitation. By moving the neurons around the gravitational space and merging them according to the gravitation, the gSOM is able to find arbitrary shape and determine the class number automatically. To confirm the validity of the presented approach, three aerial seismic images in Wenchuan covering several disaster types are utilized. The obtained quantitative and qualitative experimental results demonstrated the feasibility and accuracy of the proposed seismic image classification method.

Keywords

Earthquake High resolution image classification Gravitational self-organizing map Adaptive dynamic region merging 

Notes

Acknowledgments

This study was funded by the National Natural Science Foundation of China (41471353,61672008), the Natural Science Foundation of Shandong Province (ZR201709180096, ZR201702100118), the Fundamental Research Funds for the Central Universities (18CX05030A, 18CX02179A), the Postdoctoral Application and Research Projects of Qingdao (BY20170204), and Guangdong Provincial Application-oriented Technical Research and Development Special fund project (2016B010127006, 2017A050501039).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Aizhu Zhang
    • 1
  • Yanling Hao
    • 1
  • Genyun Sun
    • 1
  • Jinchang Ren
    • 2
    Email author
  • Huimin Zhao
    • 3
    • 4
  • Sophia Zhao
    • 2
  • Tariq S. Durrani
    • 2
  1. 1.School of GeosciencesChina University of PetroleumQingdaoChina
  2. 2.Department of Electronic and Electrical EngineeringUniversity of StrathclydeGlasgowUK
  3. 3.School of ComputersGuangdong Polytechnic Normal UniversityGuangzhouChina
  4. 4.Guangzhou Key Lab Digital Contents and SecurityGuangzhouChina

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