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Paper Abstract and Keywords
Presentation 2018-03-14 10:00
Hierarchical quaternion neural networks with self-organizing codebook for unsupervised PolSAR land classification
Hyunsoo Kim, Akira Hirose (Tokyo Univ.) NC2017-88
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a self-organizing codebook-based hierarchical polarization feature vector generation to realize an unsupervised land classification with PolSAR (polarimetric synthetic aperture radar) data. PolSAR has reached the high-resolution of decimeter level. Conventional methods perform the spatial averaging in 10m to 20m square real-space area to classify observed land into categories such as farm, forest, and town. However, with this averaging, we can not expect to discover new detailed land classes by resolution improvement, since the resolution of the PolSAR data is lowered in the averaging process. Our proposal in this paper generates feature vectors useful for classifying the land pieces into categories while preserving the detailed polarization features in respective pixels of the high-resolution PolSAR data. Then, the method discovers the new detailed land classes that can be available only in the high-resolution PolSAR data.
Keyword (in Japanese) (See Japanese page) 
(in English) polarimetric synthetic aperture radar (PolSAR) / unsupervised land classification / high-resolution / hierarchical polarization feature / quaternion neural networks / Poincare parameter / auto-encoder / self-organizing map (SOM)  
Reference Info. IEICE Tech. Rep., vol. 117, no. 508, NC2017-88, pp. 121-126, March 2018.
Paper # NC2017-88 
Date of Issue 2018-03-06 (NC) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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reproduction
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)
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Conference Information
Committee MBE NC  
Conference Date 2018-03-13 - 2018-03-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Kikai-Shinko-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ME, general 
Paper Information
Registration To NC 
Conference Code 2018-03-MBE-NC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Hierarchical quaternion neural networks with self-organizing codebook for unsupervised PolSAR land classification 
Sub Title (in English)  
Keyword(1) polarimetric synthetic aperture radar (PolSAR)  
Keyword(2) unsupervised land classification  
Keyword(3) high-resolution  
Keyword(4) hierarchical polarization feature  
Keyword(5) quaternion neural networks  
Keyword(6) Poincare parameter  
Keyword(7) auto-encoder  
Keyword(8) self-organizing map (SOM)  
1st Author's Name Hyunsoo Kim  
1st Author's Affiliation The University of Tokyo (Tokyo Univ.)
2nd Author's Name Akira Hirose  
2nd Author's Affiliation The University of Tokyo (Tokyo Univ.)
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Speaker Author-1 
Date Time 2018-03-14 10:00:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2017-88 
Volume (vol) vol.117 
Number (no) no.508 
Page pp.121-126 
#Pages
Date of Issue 2018-03-06 (NC) 


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