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Paper Abstract and Keywords
Presentation 2021-11-05 11:15
Quaternion convolutional neural networks for PolSAR land classification
Yuya Matsumoto, Ryo Natsuaki, Akira Hirose (UTokyo) EMT2021-43
Abstract (in Japanese) (See Japanese page) 
(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 the
components of input vectors in the convolutional processing. A QCNN performs an orthogonal transformation to the
components of input vectors by quaternionic rotation and scaling to learn the relationship between them. A QCNN
also can learn spatial textures of input data as well as a conventional RVCNN. In our experiments, we compare three
neural networks, namely, a fully-connected quaternion neural network (QNN), a RVCNN, and our proposed QCNN. As
experimental results, our proposed QCNN show the best classification performance. We also show that quaternionic
convolution can extract spatial texture by visualizing quaternion kernels.
Keyword (in Japanese) (See Japanese page) 
(in English) Polarimetric synthetic aperture radar (PolsAR) / convolutional neural network (CNN) / quaternion neural network (QNN) / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 226, EMT2021-43, pp. 76-81, Nov. 2021.
Paper # EMT2021-43 
Date of Issue 2021-10-28 (EMT) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF EMT2021-43

Conference Information
Committee EMT IEE-EMT  
Conference Date 2021-11-04 - 2021-11-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Electromagnetic Theory, etc. 
Paper Information
Registration To EMT 
Conference Code 2021-11-EMT-EMT 
Language Japanese 
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)
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Date Time 2021-11-05 11:15:00 
Presentation Time 25 
Registration for EMT 
Paper # IEICE-EMT2021-43 
Volume (vol) IEICE-121 
Number (no) no.226 
Page pp.76-81 
#Pages IEICE-6 
Date of Issue IEICE-EMT-2021-10-28 

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