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Proceedings Paper

Fusion of underwater object classification methods in sonar images
Author(s): Keqing Zhu; Jie Tian; Haining Huang
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Paper Abstract

In this paper, an advanced method is proposed concerned with the problem of classifying objects laying on the seabottom. This method is a fusion of different methods (KNN, SVM and improved CNN) mainly consisting of three steps: Firstly, acoustic images of underwater objects are pre-processed and segmented into shadow and sea-bottom expressed as binary images. Then Zernike moments of these binary images are computed as feature vectors and they are classified by a k-nearest neighbor (KNN) classifier. At the same time, a support vector machine(SVM) and an improved convolutional neural network (CNN) are also used to classify binary images. Finally, a vote classifier combines classification result of the three classifiers (KNN, SVM and improved CNN) and give the result. The method is applied to synthetic aperture sonar(SAS) datasets for validation. Comparing with each individual classifier (KNN, SVM and improved CNN), the proposed method performs stable and achieves better accuracy.

Paper Details

Date Published: 6 May 2019
PDF: 9 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110691X (6 May 2019); doi: 10.1117/12.2524270
Show Author Affiliations
Keqing Zhu, Institute of Acoustics (China)
Univ. of Chinese Academy of Sciences (China)
Jie Tian, Institute of Acoustics (China)
Haining Huang, Institute of Acoustics (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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