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Paper Abstract and Keywords
Presentation 2017-08-24 13:50
Deep Learning for Target Classification from SAR Imagery -- Data Augmentation and Translation Invariance --
Hidetoshi Furukawa (Toshiba Infrastructure Systems & Solutions) SANE2017-30
Abstract (in Japanese) (See Japanese page) 
(in English) This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery. In particular, the translation invariance of CNNs for SAR ATR represents the robustness against misalignment of target chips extracted from SAR images. To understand the translation invariance of the CNNs, we trained CNNs which classify the target chips from the MSTAR into the ten classes under the condition of with and without data augmentation, and then visualized the translation invariance of the CNNs. According to our results, even if we use a deep residual network, the translation invariance of the CNN without data augmentation using the aligned images such as the MSTAR target chips is not so large. A more important factor of translation invariance is the use of augmented training data. Furthermore, our CNN using augmented training data achieved a state-of-the-art classification accuracy of 99.6%. These results show an importance of domain-specific data augmentation.
Keyword (in Japanese) (See Japanese page) 
(in English) synthetic aperture radar (SAR) / automatic target recognition (ATR) / target classification / deep learning / convolutional neural network (CNN) / data augmentation / translation invariance / residual network  
Reference Info. IEICE Tech. Rep., vol. 117, no. 182, SANE2017-30, pp. 13-17, Aug. 2017.
Paper # SANE2017-30 
Date of Issue 2017-08-17 (SANE) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee SANE  
Conference Date 2017-08-24 - 2017-08-25 
Place (in Japanese) (See Japanese page) 
Place (in English) OIT UMEDA Campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Navigation, Trafic control, Radar, Remote Sensing and general issues 
Paper Information
Registration To SANE 
Conference Code 2017-08-SANE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep Learning for Target Classification from SAR Imagery 
Sub Title (in English) Data Augmentation and Translation Invariance 
Keyword(1) synthetic aperture radar (SAR)  
Keyword(2) automatic target recognition (ATR)  
Keyword(3) target classification  
Keyword(4) deep learning  
Keyword(5) convolutional neural network (CNN)  
Keyword(6) data augmentation  
Keyword(7) translation invariance  
Keyword(8) residual network  
1st Author's Name Hidetoshi Furukawa  
1st Author's Affiliation Toshiba Infrastructure Systems & Solutions Corporation (Toshiba Infrastructure Systems & Solutions)
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Date Time 2017-08-24 13:50:00 
Presentation Time 25 minutes 
Registration for SANE 
Paper # SANE2017-30 
Volume (vol) vol.117 
Number (no) no.182 
Page pp.13-17 
Date of Issue 2017-08-17 (SANE) 

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