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Committee Date Time Place Paper Title / Authors Abstract Paper #
SANE 2018-05-14
11:50		 Tokyo Kikai-Shinko-Kaikan Bldg. SAVERS: SAR ATR with Verification Support Based on Convolutional Neural Network
Hidetoshi Furukawa SANE2018-5		 We propose a new convolutional neural network (CNN) which performs coarse and fine segmentation for end-to-end synthetic... [more] SANE2018-5
pp.23-28
SANE 2018-01-25
14:50		 Nagasaki Nagasaki Prefectural Art Museum Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery
Hidetoshi Furukawa (Toshiba Infrastructure Systems & Solutions) SANE2017-92		 The standard architecture of synthetic aperture radar (SAR) automatic target recognition (ATR) consists of three stages:... [more] SANE2017-92
pp.35-40
SANE 2017-08-24
13:50		 Osaka OIT UMEDA Campus Deep Learning for Target Classification from SAR Imagery -- Data Augmentation and Translation Invariance --
Hidetoshi Furukawa (Toshiba Infrastructure Systems & Solutions) SANE2017-30		 This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (... [more] SANE2017-30
pp.13-17
SANE 2012-10-11
09:20		 Overseas The SONGDO CONVENSIA, Incheon Korea Accurate and Robust Automatic Target Recognition Method for SAR Imagery with SOM-Based Classification
Shouhei Kidera, Tetsuo Kirimoto (Univ. of Electro-Comm.) SANE2012-108		 Microwave imaging techniques, in particular synthetic aperture radar (SAR), are able to obtain useful images even in adv... [more] SANE2012-108
pp.281-284
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