Using multiple superpixel segmentation and merging of digital image method to auto-estimate gravel grain size

  • Chao Wang
  • Rui YuanEmail author
  • Yuqiu Sun
  • Changsheng Chen
  • Huimin Zhang
Original Paper


The study of grain size distribution of gravels is an important and challenging issue in stratigraphy and morphology, especially in the field of automated measurement. It largely reduces many manual processes and time consumption. Using digital image processing method to extract effective information from digital images is a more advanced method. In this study, a digital image method using automated multiple superpixel segmentation and merging is proposed for extraction of the grain size data. It adds grain size estimation and improved merging rules and repeated execution rules to the superpixel segmentation and merging. It has made great improvements in many respects, especially in the accuracy of edge segmentation and measurement. Compared with manual measurement and other image processing ways, the method proposed in this paper is an efficient approach for precisely measuring the grain size distribution of riverbed material.


Grain size distribution Superpixel segmentation and merging Auto-estimation 



We are grateful to the anonymous reviewers for their constructive reviews on the manuscript and the editors for carefully revising the manuscript.

Funding information

This research is financially supported by the Scientific Research Project of Hubei Provincial Department of Education (no. Q20181310) and Open Fund of Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of Education (no. K2018-21). The supports are gratefully acknowledged.


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Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  • Chao Wang
    • 1
  • Rui Yuan
    • 1
    • 2
    Email author
  • Yuqiu Sun
    • 1
  • Changsheng Chen
    • 1
  • Huimin Zhang
    • 1
  1. 1.School of Information and MathematicsYangtze UniversityJingzhouChina
  2. 2.Key Laboratory of Exploration Technologies for Oil and Gas ResourcesYangtze University, Ministry of EducationWuhanChina

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