Deep Learning Methods for Underwater Target Feature Extraction and Recognition

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwat...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2018-01, Vol.2018 (2018), p.1-10
Main Authors: Kang, Baolin, Shi, Jianfei, Qiu, Mengran, Wang, Kejun, Hu, Gang, Peng, Yuan
Format: Article
Language:English
Subjects:
Acoustic noise
Acoustics
Analysis
Automatic classification
Classification
Classifiers
Convolution
Data processing
Deep learning
Feature extraction
Feature recognition
Learning algorithms
Machine Learning
Methods
Neural networks
Neural Networks, Computer
Object recognition (Computers)
Pattern recognition
Pattern Recognition, Automated - methods
Ships
Signal processing
Signal Processing, Computer-Assisted
Target recognition
Teaching methods
Underwater acoustics
Voice recognition
Water
Wavelet transforms
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Summary:The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.
ISSN:1687-5265
1687-5273
DOI:10.1155/2018/1214301