NDLI: SAR-ATR method based on dual convolution capsule network

Content Provider	IET Digital Library
Author	Touafria, Mohamed Yang, Qiang
Abstract	Synthetic aperture radar (SAR) image classification is one of the most important subjects in automatic target recognition. Therefore, identifying the correct class of targets has significant importance to take a decision. Recently, several deep learning techniques, especially the convolutional neural networks (CNNs), have improved the SAR images classification performance due to its powerful perspective of feature learning and reasoning. Yet, CNN's generally need a huge amount of data for training and do not accurately manage the transformations in the input data. These drawbacks are overcome using a relatively new deep learning approach called capsule networks (CapsNets). In this study, the authors propose a method that adapts and incorporates CapsNet for the SAR image classification problem and improve recognition accuracy through a dual convolution CapsNet framework. Results obtained while experimenting on the moving and stationary target acquisition and recognition data set prove the effectiveness and the robustness of the proposed framework. The proposed experimental results demonstrate the superiority of the employed method overcoming both CNNs and CapsNet separate methods in term of classification accuracy.
Starting Page	1870
Ending Page	1878
Page Count	9
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 12, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2020.0241
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-08-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Target Recognition CapsNet Separate Method Capsule Network Classification Accuracy CNN CNNs Convolutional Neural Network Deep Learning Technique Dual Convolution CapsNet Framework Dual Convolution Capsule Network Employed Method Feature Learning Image Classification Image Recognition Important Subjects Learning in AI Neural Nets Powerful Perspective Radar Equipment Radar Imaging Radar Target Recognition Reasoning Recognition Accuracy Recognition Data Relatively New Deep Learning Approach SAR Image Classification Performance SAR Image Classification Problem SAR-ATR Method Stationary Target Acquisition Statistics Synthetic Aperture Radar Synthetic Aperture Radar Image Classification System And Application
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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