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Paper Abstract and Keywords
Presentation 2022-06-28 13:30
Joint-Conditional Mutual Information Based Feature Subset Selection for Remotely Sensed Hyperspectral Image Classification
U A Md Ehsan Ali, Keisuke Kameyama (Univ. Tsukuba) NC2022-16 IBISML2022-16
Abstract (in Japanese) (See Japanese page) 
(in English) Hundreds of contiguous bands of remotely sensed hyperspectral image (HSI) capture the spectral signatures of observed objects or materials on the earth’s surface. Although the HSI data is able to provide huge information with great details, it poses challenges to image analysis because of the high computational cost due to the large dimensionality of the feature space, redundancy in information, and curse of dimensionality. To overcome these difficulties, feature reduction techniques are used to extract informative features from hyperspectral images. This paper proposes an information-theoretic feature selection approach for selecting an informative feature subset considering the maximum of the minimum approach. The minimum of conditional mutual information based estimation is used to select a feature among the selected feature subset. This selected feature with the corresponding candidate feature is then exploited using the mutual information and joint mutual
information based valuation to find the maximum relevance of the candidate features with the target classes. The effectiveness of the proposed approach, called joint-conditional mutual information for selecting informative feature (JCIF), is assessed by implementing it in some synthetic data and two remotely sensed HSI data. Several known feature selection algorithms are also used for comparison purposes. The results of the experiments with K-Nearest Neighbors and Support Vector Machine classifiers reveal that JCIF performs better in selecting informative features.
Keyword (in Japanese) (See Japanese page) 
(in English) Hyperspectral imaging / Feature selection / Mutual Information / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 90, IBISML2022-16, pp. 115-122, June 2022.
Paper # IBISML2022-16 
Date of Issue 2022-06-20 (NC, IBISML) 
ISSN Online edition: ISSN 2432-6380
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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 NC IBISML IPSJ-BIO IPSJ-MPS  
Conference Date 2022-06-27 - 2022-06-29 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Paper Information
Registration To IBISML 
Conference Code 2022-06-NC-IBISML-BIO-MPS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Joint-Conditional Mutual Information Based Feature Subset Selection for Remotely Sensed Hyperspectral Image Classification 
Sub Title (in English)  
Keyword(1) Hyperspectral imaging  
Keyword(2) Feature selection  
Keyword(3) Mutual Information  
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1st Author's Name U A Md Ehsan Ali  
1st Author's Affiliation University of Tsukuba (Univ. Tsukuba)
2nd Author's Name Keisuke Kameyama  
2nd Author's Affiliation University of Tsukuba (Univ. Tsukuba)
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Speaker Author-1 
Date Time 2022-06-28 13:30:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # NC2022-16, IBISML2022-16 
Volume (vol) vol.122 
Number (no) no.89(NC), no.90(IBISML) 
Page pp.115-122 
#Pages
Date of Issue 2022-06-20 (NC, IBISML) 


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