Ms. Keqing Zhu Profile

Keqing Zhu

at Institute of Acoustics

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Proceedings Article | 6 May 2019 Paper

Fusion of underwater object classification methods in sonar images

Keqing Zhu, Jie Tian, Haining Huang

Proceedings Volume 11069, 110691X (2019) https://doi.org/10.1117/12.2524270

KEYWORDS: Image segmentation, Image classification, Computer simulations, Acoustics, Convolutional neural networks, Feature extraction

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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.

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