Deep Learning Methods for Underwater Target Feature Extraction and Recognition

• 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
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2018/1214301

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.