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Paper Abstract and Keywords
Presentation 2018-05-14 11:50
SAVERS: SAR ATR with Verification Support Based on Convolutional Neural Network
Hidetoshi Furukawa SANE2018-5
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a new convolutional neural network (CNN) which performs coarse and fine segmentation for end-to-end synthetic aperture radar (SAR) automatic target recognition (ATR) system. In recent years, many CNNs for SAR ATR using deep learning have been proposed, but most of them classify target classes from fixed size target chips extracted from SAR imagery. On the other hand, we proposed the CNN which outputs the score of the multiple target classes and a background class for each pixel from the SAR imagery of arbitrary size and multiple targets as fine segmentation. However, it was necessary for humans to judge the CNN segmentation result. In this report, we propose a CNN called SAR ATR with verification support (SAVERS), which performs region-wise (i.e. coarse) segmentation and pixel-wise segmentation. SAVERS discriminates between target and non-target, and classifies multiple target classes and non-target class by coarse segmentation. This report describes the evaluation results of SAVERS using the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset.
Keyword (in Japanese) (See Japanese page) 
(in English) automatic target recognition (ATR) / multiple targets / detection / discrimination / classification / convolutional neural network (CNN) / deep learning / synthetic aperture radar (SAR)  
Reference Info. IEICE Tech. Rep., vol. 118, no. 28, SANE2018-5, pp. 23-28, May 2018.
Paper # SANE2018-5 
Date of Issue 2018-05-07 (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 2018-05-14 - 2018-05-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Kikai-Shinko-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Radar, EW and general 
Paper Information
Registration To SANE 
Conference Code 2018-05-SANE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) SAVERS: SAR ATR with Verification Support Based on Convolutional Neural Network 
Sub Title (in English)  
Keyword(1) automatic target recognition (ATR)  
Keyword(2) multiple targets  
Keyword(3) detection  
Keyword(4) discrimination  
Keyword(5) classification  
Keyword(6) convolutional neural network (CNN)  
Keyword(7) deep learning  
Keyword(8) synthetic aperture radar (SAR)  
1st Author's Name Hidetoshi Furukawa  
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Speaker Author-1 
Date Time 2018-05-14 11:50:00 
Presentation Time 25 minutes 
Registration for SANE 
Paper # SANE2018-5 
Volume (vol) vol.118 
Number (no) no.28 
Page pp.23-28 
Date of Issue 2018-05-07 (SANE) 

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