吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (3): 834-839.doi: 10.13229/j.cnki.jdxbgxb201403041

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Improved minimum symmetric Tsallis cross entropy for segmentation of a sonar image from a small underwater target

ZHANG Jin-guo1,GUO Hai-tao2,3,WU Jun-peng3,LI Yi-tong4   

  1. 1.Power Transmission and Transformation Technology College, Northeast Dianli University, Jilin 132012, China; 2.College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China;
    3.Electrical Engineering College, Northeast Dianli University, Jilin 132012, China;
    4.Beijing University of Posts and Telecommunications, School of Computer Science, Beijing 100876, China
  • Received:2013-01-10 Online:2014-03-01 Published:2014-03-01

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Abstract: A segmentation method on the sonar image of a small underwater target is proposed. In this method, the gray-level threshold for segmentation is acquired via the minimum symmetric Tsallis cross entropy that is based on one-dimensional bound histogram. In this method, first, the speckle noise of the sonar image is suppressed. Second, the bound set is constructed according to the restriction in the gray-level values of both pixels and their neighborhood averages, and the one-dimensional bound histogram corresponding to that bound set is established. Third, the gray-level threshold for segmentation is determined according to the minimum symmetric Tsallis cross entropy based on the one-dimensional bound histogram. Finally, the isolated areas in the threshold image are removed. Experimental results show that the proposed method is well adequate for the images with a nonideal bimodal histogram; it has better antinoise performance than the existing methods based on the entropy of the one-dimensional bound histogram.

Key words: image processing technology, sonar image, image segmentation, bound histogram, entropy, threshold

CLC Number: 

  • TN911.73
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