The Classification of Underwater Acoustic Targets Based on Deep Learning Methods

DOI

10.2991/caai-17.2017.118How to use a DOI?

Keywords

underwater target; classification; recognition; deep learning; DBN, CNN

Abstract

The underwater target classification is a challenging task due to the complexity of marine environment and the diversity of underwater target features. Most of the-state-of-the-art target recognition systems depend on feature extraction schemes based on expert knowledge in order to effectively represent the target signatures. In contrast, 16 different kinds of underwater acoustic targets are categorized in this paper by using Convolution Neural Network (CNN) and Deep Brief Network (DBN), which can achieve the accuracy up to 94.75% and 96.96% respectively in both supervised and unsupervised fashions. To compare with the results of traditional machine learning methods, we also use Support Vector Machine (SVM) and Wndchrm to do the same job and the latter is originally a tool applied for the biological image analysis. The results show that deep learning methods can achieve higher recognition accuracy when classifying the underwater targets from their radiation noises.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hao Yue
AU  - Lilun Zhang
AU  - Dezhi Wang
AU  - Yongxian Wang
AU  - Zengquan Lu
PY  - 2017/06
DA  - 2017/06
TI  - The Classification of Underwater Acoustic Targets Based on Deep Learning Methods
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 526
EP  - 529
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.118
DO  - 10.2991/caai-17.2017.118
ID  - Yue2017/06
ER  -