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Paper Abstract and Keywords
Presentation 2015-11-24 13:45
Adaptive Nearest Feature Space Method for Remote Sensing Images Classification
Yang-Lang Chang (NTUT), Chihyuan Chu (G-AVE), Hirokazu Kobayashi (OIT), Tzu-Wei Tseng (NTUT) SANE2015-62
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper a novel technique based on nearest feature space (NFS), known as adaptive nearest feature space (ANFS), is proposed for supervised remote sensing image classification. The NFS has been proven to be efficient for remote sensing image classification in recent years. Although NFS can perform well for classification, in some instances, it decreases the efficiency when samples of different classes are not far apart or even overlapped. Due to the different neighborhood structures of overlapping training labels, the traditional NFS can't perform well for classification of remote sensing images. In response, ANFS is proposed to overcome this problem. It combines and adopts two methods, NFS and incenter-based nearest feature space (INFS) which makes use of the incircle of three labeled samples to form an INFS, to achieve the best classification accuracy. In ANFS, a fitting preprocessing of NFS is presented to determine what the best-fix model (NFS/INFS) is for the three nearest labeled samples of the same classes. Experimental results demonstrate the proposed ANFS approach is suitable for land cover classification in earth remote sensing. It can achieve the better performance than NFS classifier when the class samples distribution overlaps.
Keyword (in Japanese) (See Japanese page) 
(in English) remote sensing images classification / nearest feature space / incenter-based nearest feature space / adaptive nearest feature space / / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 320, SANE2015-62, pp. 71-74, Nov. 2015.
Paper # SANE2015-62 
Date of Issue 2015-11-16 (SANE) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
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 SANE  
Conference Date 2015-11-23 - 2015-11-24 
Place (in Japanese) (See Japanese page) 
Place (in English) AIT, Bangkok, Thailand 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ICSANE 2015 
Paper Information
Registration To SANE 
Conference Code 2015-11-SANE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Adaptive Nearest Feature Space Method for Remote Sensing Images Classification 
Sub Title (in English)  
Keyword(1) remote sensing images classification  
Keyword(2) nearest feature space  
Keyword(3) incenter-based nearest feature space  
Keyword(4) adaptive nearest feature space  
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1st Author's Name Yang-Lang Chang  
1st Author's Affiliation National Taipei University of Technology (NTUT)
2nd Author's Name Chihyuan Chu  
2nd Author's Affiliation G-AVE Technology Company (G-AVE)
3rd Author's Name Hirokazu Kobayashi  
3rd Author's Affiliation Osaka Institute of Technology (OIT)
4th Author's Name Tzu-Wei Tseng  
4th Author's Affiliation National Taipei University of Technology (NTUT)
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Speaker Author-2 
Date Time 2015-11-24 13:45:00 
Presentation Time 25 minutes 
Registration for SANE 
Paper # SANE2015-62 
Volume (vol) vol.115 
Number (no) no.320 
Page pp.71-74 
#Pages 4 
Date of Issue 2015-11-16 (SANE) 

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