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| Presentation | 2018-05-14 11:50 SAVERS: SAR ATR with Verification Support Based on Convolutional Neural Network Hidetoshi Furukawa  SANE2018-5 |
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| 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 | 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. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | SANE2018-5 |
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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 |
| #Pages | 6 |
| Date of Issue | 2018-05-07 (SANE) |
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