NDLI: Efficient deep convolutional neural networks using CReLU for ATR with limited SAR images

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Efficient deep convolutional neural networks using CReLU for ATR with limited SAR images

Content Provider	IET Digital Library
Author	Wang, Zelong Xu, Xianghui
Abstract	The computational cost of many deep convolutional neural networks (CNNs) for the automatic target recognition proposed in the area of synthetic aperture radar (SAR) imagery recently is huge, and the limited SAR images are always insufficient for training the deep CNNs. To improve the computational efficiency, a new light but very efficient convolutional network architecture is designed using some novel techniques to get the better results. The authors apply the batch normalisation before each convolutional layer in order to reduce ‘internal covariate shift’ and use the drop-out strategy in the fully layer to avoid over-fitting. Additionally, concatenated ReLU is used as activation scheme specially instead of ReLU for preserving the negative phase information to get the double feature maps of the previous layer rather than to increase the depth of the filters that can lessen the parameters of the networks. The results of the experimental demonstrate that the authors’ CNNs can both achieve a state-of-the-art classification accuracy of 99.53% of the SAR target classification in the Moving and Stationary Target Acquisition and Recognition ten classes public dataset and perform well even when the training data is sparse.
Starting Page	7615
Ending Page	7618
Page Count	4
Volume Number	2019
e-ISSN	20513305
Issue Number	Issue 21, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2019/21
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0567
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2019-07-11
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Authors Automatic Target Recognition Computational Cost Computational Efficiency Computer Vision And Image Processing Technique Convolution Convolutional Layer Deep CNNs Efficient Convolutional Network Architecture Efficient Deep Convolutional Neural Network Feature Extraction Fully Layer Image Classification Image Recognition Internal Covariate Shift Knowledge Engineering Technique Learning in AI Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Radar Equipment Radar Imaging Radar Target Recognition SAR Image SAR Target Classification Synthetic Aperture Radar Synthetic Aperture Radar Imagery System And Application
Content Type	Text
Resource Type	Article

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