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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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Automated Approach To Classification Of Mine-Like Objects Using Multiple-Aspect Sonar Images

Xiaoguang Wang
  • Faculty of Computer Science Dalhousie University, Canada
  • Email:
/ Xuan Liu
  • Faculty of Computer Science Dalhousie University, Canada
  • Email:
/ Nathalie Japkowicz
  • School of Electrical Engineering & Computer Science University of Ottawa, Canada
  • Email:
/ Stan Matwin
  • Faculty of Computer Science, Dalhousie University, Canada Institute of Computer Science, Polish Academy of Sciences, Poland
  • Email:
Published Online: 2015-03-01 | DOI: https://doi.org/10.1515/jaiscr-2015-0004

Abstract

In this paper, the detection of mines or other objects on the seabed from multiple side-scan sonar views is considered. Two frameworks are provided for this kind of classification. The first framework is based upon the Dempster–Shafer (DS) concept of fusion from a single-view kernel-based classifier and the second framework is based upon the concepts of multi-instance classifiers. Moreover, we consider the class imbalance problem which is always presents in sonar image recognition. Our experimental results show that both of the presented frameworks can be used in mine-like object classification and the presented methods for multi-instance class imbalanced problem are also effective in such classification.

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About the article

Published Online: 2015-03-01

Published in Print: 2014-04-01



Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2015-0004. Export Citation

© Academy of Management (SWSPiZ), Lodz. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

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