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Paper Abstract and Keywords
Presentation 2018-10-12 13:30
Automatic Target Recognition based on Generative Adversarial Networks for Synthetic Aperture Radar Images
Yang-Lang Chang, Bo-Yao Chen, Chih-Yuan Chu, Sina Hadipour (NTUT), Hirokazu Kobayashi (OIT) SANE2018-51
Abstract (in Japanese) (See Japanese page) 
(in English) Synthetic Aperture Radar (SAR) is an all day and all weather condition imaging technique which is widely used in national defense, remote sensing, disaster prevention, interferometry and forest and urban footprint mapping. Recently, convolutional neural networks have been used for automatic target recognition (SAR-ATR) and classification. The drawback, however, is the difficulty obtaining sufficient and reliable data in order to train a high accuracy classifier for automatic target recognition. As the number of training samples is reduced, the SAR-ATR accuracy rate decreases rapidly. Our study proposes a deep learning model based on Generative Adversarial Network (GAN) to overcome the problem of insufficient training samples and improve the performance of target classification. GAN is composed of two networks: A Generator network and a Discriminator network. The generator network produces SAR images from a series of random numbers. The discriminator network is a classifier which is trained using supervised learning to classify real and fake SAR images. The Generator and the Discriminator compete with each other in the training process in order to learn robust and reliable target features in SAR images. However, traditional GAN cannot be used to solve the classification problems in SAR-ATR. Our network is a variant of GAN called Auxiliary Classifier GAN (AC-GAN). The structure of AC-GAN allows separating large datasets into subsets by class and training a generator and discriminator for each subset. In this experiment, the SAR images in Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset were used to train the network. Using all the images in the dataset for training resulted in a classification accuracy of 98%. When only less than one-fifth of the images were used, AC-GAN reached an accuracy of 90%. This is a considerable increase in accuracy in comparison with traditional CNNs where for the same number of training samples, the accuracy rapidly decreased to 80%.
Keyword (in Japanese) (See Japanese page) 
(in English) synthetic aperture radar / automatic target recognition / generative adversarial networks (GAN) / auxiliary classifiers GAN / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 239, SANE2018-51, pp. 41-44, Oct. 2018.
Paper # SANE2018-51 
Date of Issue 2018-10-05 (SANE) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
and
reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee SANE  
Conference Date 2018-10-12 - 2018-10-12 
Place (in Japanese) (See Japanese page) 
Place (in English) The University of Electro-Communications 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Radar signal processing and general issues 
Paper Information
Registration To SANE 
Conference Code 2018-10-SANE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automatic Target Recognition based on Generative Adversarial Networks for Synthetic Aperture Radar Images 
Sub Title (in English)  
Keyword(1) synthetic aperture radar  
Keyword(2) automatic target recognition  
Keyword(3) generative adversarial networks (GAN)  
Keyword(4) auxiliary classifiers GAN  
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1st Author's Name Yang-Lang Chang  
1st Author's Affiliation National Taipei University of Technology (NTUT)
2nd Author's Name Bo-Yao Chen  
2nd Author's Affiliation National Taipei University of Technology (NTUT)
3rd Author's Name Chih-Yuan Chu  
3rd Author's Affiliation National Taipei University of Technology (NTUT)
4th Author's Name Sina Hadipour  
4th Author's Affiliation National Taipei University of Technology (NTUT)
5th Author's Name Hirokazu Kobayashi  
5th Author's Affiliation Osaka Institute of Technology (OIT)
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Speaker
Date Time 2018-10-12 13:30:00 
Presentation Time 25 
Registration for SANE 
Paper # IEICE-SANE2018-51 
Volume (vol) IEICE-118 
Number (no) no.239 
Page pp.41-44 
#Pages IEICE-4 
Date of Issue IEICE-SANE-2018-10-05 


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