Computer Science ›› 2019, Vol. 46 ›› Issue (11): 277-283.doi: 10.11896/jsjkx.181001985

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Method of Automatically Extracting Urban Water Bodies from High-resolution Images with Complex Background

WANG Wei-hong, CHEN Xiao, WU Wei, GAO Xing-yu   

  1. (College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)
  • Received:2018-10-25 Online:2019-11-15 Published:2019-11-14

Abstract: The distribution of urban water bodies is of great significance for people to understand the geographical phenomena such as the urban water circulation and the Heat-island Effect.It is common to obtain information by using high-resolution images for water extraction and water mapping.However,automatically extraction of water bodies by using the high-resolution images still is difficult for the complex background of the urban area,fewer spectral channels provided by the high-resolution images and the uneven distribution of water bodies in the images.This paper proposed an automatic extraction method of urban water bodies in complex background based on high-resolution images.First,adaptive threshold is selected for segmentation to gain the initial region of water,since water has a low gray value of the near infrared channel.Next,on the initial region,a buffering algorithm are used to obtain the target region of water extraction,and gauss mixture model and an expectation maximization algorithm is used to improve the distributionpara-meters of water.Then,the water bodies are extracted automatically using the maximum likelihood method with these parameters.As for the large number of shadow elements mixed in the rough extraction,a fusion features method is proposed to eliminate those noise points and obtain more accurate extraction result.The experiment results of water extraction in Jinshan show that the proposed method can effectively extract the structure of water bodies with small proportion in the experimental images,and perform well with high accuracy comparing to the commonly used automatic extraction algorithms.

Key words: Class imbalance, Extraction of urban water bodies, Gauss mixture model, Modified expectation maximization algorithm, Shadow removal

CLC Number: 

  • TP751
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