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Paper Abstract and Keywords
Presentation 2019-08-22 11:55
Fundamental Study on Land Use Classification of PolSAR Data by Using Machine Learning
Nanako Saito, Masanori Gocho, Hiroyoshi Yamada, Ryoichi Sato, Yoshio Yamaguchi (Niigata Univ.) AP2019-57
Abstract (in Japanese) (See Japanese page) 
(in English) Polarimetric Synthetic Aperture Radar (PolSAR) has been attracting attention in ground target detection and classification. This is because it can observe wide area regardless of time zone and weather, and analyze in detail by using polarimetric information. Various methods, such as model-based scattering power decomposition, eigenvalue analysis and so forth, have been proposed for the analysis of PolSAR data. Recently, classification techniques by using machine learning have been intensively studied to reduce misclassification. In this paper, we present some experimental results of land use classification of ALOS-2/PALSAR-2 data by using convolutional neural network (CNN), and compare with the case of scattering power decomposition and support vector machine (SVM).
Keyword (in Japanese) (See Japanese page) 
(in English) Polarimetric Synthetic Aperture Radar (PolSAR) / ALOS-2 / Convolutional neural network / Support vector machine / Land use map / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 168, AP2019-57, pp. 55-60, Aug. 2019.
Paper # AP2019-57 
Date of Issue 2019-08-14 (AP) 
ISSN 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 AP  
Conference Date 2019-08-21 - 2019-08-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkai-Gakuen Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Antennas and Propagation 
Paper Information
Registration To AP 
Conference Code 2019-08-AP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fundamental Study on Land Use Classification of PolSAR Data by Using Machine Learning 
Sub Title (in English)  
Keyword(1) Polarimetric Synthetic Aperture Radar (PolSAR)  
Keyword(2) ALOS-2  
Keyword(3) Convolutional neural network  
Keyword(4) Support vector machine  
Keyword(5) Land use map  
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Keyword(7)  
Keyword(8)  
1st Author's Name Nanako Saito  
1st Author's Affiliation Niigata University (Niigata Univ.)
2nd Author's Name Masanori Gocho  
2nd Author's Affiliation Niigata University (Niigata Univ.)
3rd Author's Name Hiroyoshi Yamada  
3rd Author's Affiliation Niigata University (Niigata Univ.)
4th Author's Name Ryoichi Sato  
4th Author's Affiliation Niigata University (Niigata Univ.)
5th Author's Name Yoshio Yamaguchi  
5th Author's Affiliation Niigata University (Niigata Univ.)
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Speaker Author-1 
Date Time 2019-08-22 11:55:00 
Presentation Time 25 minutes 
Registration for AP 
Paper # AP2019-57 
Volume (vol) vol.119 
Number (no) no.168 
Page pp.55-60 
#Pages
Date of Issue 2019-08-14 (AP) 


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