NDLI: Underwater target classification at greater depths using deep neural network with joint multiple-domain feature

Content Provider	IET Digital Library
Author	Cao, Xu Zhang, Xiaomin Togneri, Roberto Yu, Yang
Abstract	For underwater target classification which is supposed to recognise different ships with the radiated acoustic signal, it is the most challenging task to provide excellent classification accuracy in a variety of environments. However, most of the existing systems are optimised to get the best performance on the data set from certain situations which they are trained in, which may lead to generalisation risks when applied to new environments. Here, the authors introduce an underwater target classification framework using a deep neural network to learn deep features from a large joint multiple-domain input. The authors propose to incorporate spectral and wavelet domain information with different resolutions to grasp the ‘global’ structure and the ‘local’ transient variation of the raw radiated signals. In contrast to shallow models, a stacked sparse autoencoder (SSAE) model, which is composed of multiple hidden layers and a softmax classifier, is adopted to learn more discriminating features for classification. In the authors’ experiments, the proposed SSAE model is evaluated on the data set consisting of underwater acoustic signal received at different ocean depths. The authors’ results show that the proposed SSAE model with joint input features achieved a 5% improvement in classification accuracy compared to the state-of-the-art DBN approach.
Starting Page	484
Ending Page	491
Page Count	8
ISSN	17518784
Volume Number	13
e-ISSN	17518792
Issue Number	Issue 3, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/13/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2018.5279
Journal	IET Radar, Sonar & Navigation
Publisher Date	2018-11-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Acoustic Signal Processing Belief Network Computer Vision And Image Processing Technique Deep Features Deep Neural Network Different Ocean Depths Different Resolutions Different Ships Discriminating Features Domain Information Excellent Classification Accuracy Feature Extraction Greater Depths Image Classification Joint Input Feature Joint Multiple-domain Input Knowledge Engineering Technique Learning in AI Multiple Hidden Layer Multiple-domain Feature Neural Computing Technique Neural Nets Pattern Classification Radiated Acoustic Signal Raw Radiated Signal Signal Classification SSAE Model Stacked Sparse Autoencoder Model Statistics Underwater Acoustic Signal Underwater Target Classification Framework Wavelet Transform
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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