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| Paper Abstract and Keywords | ||
| Presentation | 2018-01-25 14:50 Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery Hidetoshi Furukawa (Toshiba Infrastructure Systems & Solutions)  SANE2017-92 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The standard architecture of synthetic aperture radar (SAR) automatic target recognition (ATR) consists of three stages: detection, discrimination, and classification. In recent years, convolutional neural networks (CNNs) for SAR ATR have been proposed, but most of them classify target classes from a target chip extracted from SAR imagery, as a classification for the third stage of SAR ATR. In this report, we propose a novel CNN for end-to-end ATR from SAR imagery. The CNN named verification support network (VersNet) performs all three stages of SAR ATR end-to-end. VersNet inputs a SAR image of arbitrary sizes with multiple classes and multiple targets, and outputs a SAR ATR image representing the position, class, and pose of each detected target. This report describes the evaluation results of VersNet which trained to output scores of all 12 classes: 10 target classes, a target front class, and a background class, for each pixel using the moving and stationary target acquisition and recognition (MSTAR) public dataset. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | automatic target recognition (ATR) / multiple targets / detection / classification / pose estimation / convolutional neural network (CNN) / deep learning / synthetic aperture radar (SAR) | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 403, SANE2017-92, pp. 35-40, Jan. 2018. | |
| Paper # | SANE2017-92 | |
| Date of Issue | 2018-01-18 (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. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | SANE2017-92 |
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| Conference Information | |
| Committee | SANE |
| Conference Date | 2018-01-25 - 2018-01-26 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nagasaki Prefectural Art Museum |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Positioning, navigation, Radar and general |
| Paper Information | |
| Registration To | SANE |
| Conference Code | 2018-01-SANE |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery |
| Sub Title (in English) | |
| Keyword(1) | automatic target recognition (ATR) |
| Keyword(2) | multiple targets |
| Keyword(3) | detection |
| Keyword(4) | classification |
| Keyword(5) | pose estimation |
| Keyword(6) | convolutional neural network (CNN) |
| Keyword(7) | deep learning |
| Keyword(8) | synthetic aperture radar (SAR) |
| 1st Author's Name | Hidetoshi Furukawa |
| 1st Author's Affiliation | Toshiba Infrastructure Systems & Solutions Corporation (Toshiba Infrastructure Systems & Solutions) |
| Speaker | Author-1 |
| Date Time | 2018-01-25 14:50:00 |
| Presentation Time | 25 minutes |
| Registration for | SANE |
| Paper # | SANE2017-92 |
| Volume (vol) | vol.117 |
| Number (no) | no.403 |
| Page | pp.35-40 |
| #Pages | 6 |
| Date of Issue | 2018-01-18 (SANE) |
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