Presentation | 2021-11-05 Quaternion convolutional neural networks for PolSAR land classification Yuya Matsumoto, Ryo Natsuaki, Akira Hirose, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We propose a quaternion convolutional neural network (QCNN) for Polarimetric synthetic aperture radar(PolSAR) land classification. Unlike a conventional real-valued CNN (RVCNN), a QCNN does not simply sum up thecomponents of input vectors in the convolutional processing. A QCNN performs an orthogonal transformation to thecomponents of input vectors by quaternionic rotation and scaling to learn the relationship between them. A QCNNalso can learn spatial textures of input data as well as a conventional RVCNN. In our experiments, we compare threeneural networks, namely, a fully-connected quaternion neural network (QNN), a RVCNN, and our proposed QCNN. Asexperimental results, our proposed QCNN show the best classification performance. We also show that quaternionicconvolution can extract spatial texture by visualizing quaternion kernels. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Polarimetric synthetic aperture radar (PolsAR) / convolutional neural network (CNN) / quaternion neural network (QNN) |
Paper # | EMT2021-43 |
Date of Issue | 2021-10-28 (EMT) |
Conference Information | |
Committee | EMT / IEE-EMT |
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Conference Date | 2021/11/4(2days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | Online |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | Electromagnetic Theory, etc. |
Chair | Hiroyuki Deguchi(Doshisha Univ.) / Akira Matsushima(Kumamoto Univ.) |
Vice Chair | Hideki Kawaguchi(Muroran Inst. of Tech) |
Secretary | Hideki Kawaguchi(Miyazaki Univ.) / (Mitsubishi Electric) |
Assistant | Kazuki Niino(Kyoto Univ.) / Junichiro Sugisaka(Kitami Inst. of Tech.) |
Paper Information | |
Registration To | Technical Committee on Electromagnetic Theory / Technical Meeting on Electromagnetic Theory |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Quaternion convolutional neural networks for PolSAR land classification |
Sub Title (in English) | |
Keyword(1) | Polarimetric synthetic aperture radar (PolsAR) |
Keyword(2) | convolutional neural network (CNN) |
Keyword(3) | quaternion neural network (QNN) |
1st Author's Name | Yuya Matsumoto |
1st Author's Affiliation | The University of Tokyo(UTokyo) |
2nd Author's Name | Ryo Natsuaki |
2nd Author's Affiliation | The University of Tokyo(UTokyo) |
3rd Author's Name | Akira Hirose |
3rd Author's Affiliation | The University of Tokyo(UTokyo) |
Date | 2021-11-05 |
Paper # | EMT2021-43 |
Volume (vol) | vol.121 |
Number (no) | EMT-226 |
Page | pp.pp.76-81(EMT), |
#Pages | 6 |
Date of Issue | 2021-10-28 (EMT) |