| Presentation | 2018-05-14 SAVERS: SAR ATR with Verification Support Based on Convolutional Neural Network Hidetoshi Furukawa, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | automatic target recognition (ATR) / multiple targets / detection / discrimination / classification / convolutional neural network (CNN) / deep learning / synthetic aperture radar (SAR) |
| Paper # | SANE2018-5 |
| Date of Issue | 2018-05-07 (SANE) |
| Conference Information | |
| Committee | SANE |
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| Conference Date | 2018/5/14(1days) |
| 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 |
| 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) | 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 |
| 1st Author's Affiliation | *(*) |
| Date | 2018-05-14 |
| Paper # | SANE2018-5 |
| Volume (vol) | vol.118 |
| Number (no) | SANE-28 |
| Page | pp.pp.23-28(SANE), |
| #Pages | 6 |
| Date of Issue | 2018-05-07 (SANE) |