| Presentation | 2018-01-25 Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery Hidetoshi Furukawa, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | automatic target recognition (ATR) / multiple targets / detection / classification / pose estimation / convolutional neural network (CNN) / deep learning / synthetic aperture radar (SAR) |
| Paper # | SANE2017-92 |
| Date of Issue | 2018-01-18 (SANE) |
| Conference Information | |
| Committee | SANE |
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| Conference Date | 2018/1/25(2days) |
| 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 |
| Chair | Sonosuke Fukushima(ENRI) |
| Vice Chair | Toshifumi Moriyama(Nagasaki Univ.) / Akitsugu Nadai(NICT) |
| Secretary | Toshifumi Moriyama(Mitsubishi Electric) / Akitsugu Nadai(ENRI) |
| Assistant | Manabu Akita(Univ. of Electro-Comm.) / Ryo Natsuaki(Univ. of Tokyo) |
| Paper Information | |
| Registration To | Technical Committee on Space, Aeronautical and Navigational Electronics |
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| Language | ENG |
| 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) |
| Date | 2018-01-25 |
| Paper # | SANE2017-92 |
| Volume (vol) | vol.117 |
| Number (no) | SANE-403 |
| Page | pp.pp.35-40(SANE), |
| #Pages | 6 |
| Date of Issue | 2018-01-18 (SANE) |