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Paper Abstract and Keywords
Presentation 2025-12-08 15:25
Quaternion Inverse Mapping in Polarimetric Synthetic Aperture Radar for land classification
Hiroki Onishi (UTokyo), Gunjan Joshi (HZDR), Ryo Natsuaki, Akira Hirose (UTokyo) SANE2025-95
Abstract (in Japanese) (See Japanese page) 
(in English) Quaternion Neural Networks（QNNs）are effective for land classification using Polarimetric Synthetic Aperture Radar（Pol-SAR）data, as they efficiently handle polarimetric information. However, QNNs often function as "black boxes," making it difficult to interpret their decision-making processes. This paper proposes "Quaternion Inverse Mapping," an Explainable AI（XAI）method to enhance the transparency of QNNs. This algorithm traces the signal flow backward from a specific classification output（the winning neuron）in a trained QNN to identify and visualize the input features that contributed most to that decision. We applied this method to a land classification experiment using actual airborne SAR data. The results successfully demonstrated, quantitatively on the Poincaré sphere, which input polarimetric features （Stokes parameters）strongly contributed to the classification of each class. The proposed method enables the physical interpretation of QNN classification grounds and contributes to the reliability assessment of the model.
Keyword (in Japanese) (See Japanese page) 
(in English) Inverse Mapping / Explainable AI（XAI） / Quaternion Newral Network / olarimetric Synthetic Aperture Radar（Pol-SAR） / land classifacation / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 280, SANE2025-95, pp. 64-69, Dec. 2025.
Paper # SANE2025-95 
Date of Issue 2025-12-01 (SANE) 
ISSN 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 2025-12-08 - 2025-12-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Meiji University Surugadai Campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Radar image processing, Signal processing, EM measurement, Machine learning and general 
Paper Information
Registration To SANE 
Conference Code 2025-12-SANE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Quaternion Inverse Mapping in Polarimetric Synthetic Aperture Radar for land classification 
Sub Title (in English)  
Keyword(1) Inverse Mapping  
Keyword(2) Explainable AI（XAI）  
Keyword(3) Quaternion Newral Network  
Keyword(4) olarimetric Synthetic Aperture Radar（Pol-SAR）  
Keyword(5) land classifacation  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Hiroki Onishi  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Gunjan Joshi  
2nd Author's Affiliation Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
3rd Author's Name Ryo Natsuaki  
3rd Author's Affiliation The University of Tokyo (UTokyo)
4th Author's Name Akira Hirose  
4th Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2025-12-08 15:25:00 
Presentation Time 20 minutes 
Registration for SANE 
Paper # SANE2025-95 
Volume (vol) vol.125 
Number (no) no.280 
Page pp.64-69 
#Pages 6 
Date of Issue 2025-12-01 (SANE) 

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