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Paper Abstract and Keywords
Presentation 2019-11-01 10:20
Study on Land Use Classification of PolSAR Data by Using Convolutional Neural Network
Nanako Saito, Masanori Gocho, Hiroyoshi Yamada, Ryoichi Sato, Yoshio Yamaguchi (Niigata Univ.) SANE2019-63
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 and improve classification accuracy. 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 classification performance 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. 255, SANE2019-63, pp. 77-82, Oct. 2019.
Paper # SANE2019-63 
Date of Issue 2019-10-24 (SANE) 
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)
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Conference Information
Committee SANE  
Conference Date 2019-10-31 - 2019-11-01 
Place (in Japanese) (See Japanese page) 
Place (in English) KOREA (Jeju) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ICSANE2019 
Paper Information
Registration To SANE 
Conference Code 2019-10-SANE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on Land Use Classification of PolSAR Data by Using Convolutional Neural Network 
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  
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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Date Time 2019-11-01 10:20:00 
Presentation Time 20 
Registration for SANE 
Paper # IEICE-SANE2019-63 
Volume (vol) IEICE-119 
Number (no) no.255 
Page pp.77-82 
#Pages IEICE-6 
Date of Issue IEICE-SANE-2019-10-24 

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