| Presentation | 2021-11-12 Detecting moving ships in ALOS-2 spotlight SAR images by deep learning model Takero Yoshida, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Spotlight SAR image has high resolution with long azimuth integration time. Meanwhile moving objects like ships are not focused due to the long integration time. Therefore, the moving ships in the spotlight SAR images are emerged as extended shapes in the azimuth direction. It is a typical imaging of moving ships in a spotlight mode, and therefore we can classify ships into moving and stationary cases. This paper shows the detection of moving ships by deep learning method, in particular using YOLOv5 model, which is relatively new method in a framework of YOLO family. ALOS-2/PALSAR-2 spotlight SAR images were analyzed and moving ships were detected by YOLOv5 model. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | |
| Paper # | SANE2021-57 |
| Date of Issue | 2021-11-04 (SANE) |
| Conference Information | |
| Committee | SANE |
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| Conference Date | 2021/11/11(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | ICSANE2021/Workshop on subsurface electromagnetic measurement |
| Chair | Toshifumi Moriyama(Nagasaki Univ.) |
| Vice Chair | Makoto Tanaka(Tokai Univ.) / Takeshi Amishima(Mitsubishi Electric) |
| Secretary | Makoto Tanaka(Univ. of Tokyo) / Takeshi Amishima(ENRI) |
| Assistant | Takayuki Kitamura(Mitsubishi Electric) |
| 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) | Detecting moving ships in ALOS-2 spotlight SAR images by deep learning model |
| Sub Title (in English) | |
| Keyword(1) | |
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| 1st Author's Name | Takero Yoshida |
| 1st Author's Affiliation | Tokyo University of Marine Science and Technology(TUMSAT) |
| Date | 2021-11-12 |
| Paper # | SANE2021-57 |
| Volume (vol) | vol.121 |
| Number (no) | SANE-236 |
| Page | pp.pp.136-138(SANE), |
| #Pages | 3 |
| Date of Issue | 2021-11-04 (SANE) |