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| Presentation | 2022-03-01 09:20 [Poster Presentation] Robust Hyperspectral Anomaly Detection via Component Decomposition Based on Convex Optimization Koyo Sato, Shunsuke Ono (Tokyo Tech)  EA2021-71 SIP2021-98 SP2021-56 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Anomaly detection in hyperspectral (HS) images is a technique to identify pixels whose spectral wavelengths differ from those of surrounding pixels. It is an essential process in many HS image analysis applications, and various anomaly detection methods have been proposed. However, most of them do not take into account the effect of noise in HS images, and the detection performance is significantly degraded especially in the case of non-Gaussian noise. In this report, we propose a method to achieve robust anomaly detection even when the HS image contains Gaussian-Sparse mixed noise. Specifically, we formulate the anomaly detection problem as a constrained convex optimization problem that includes two regularization functions considering the properties of the background and anomalous pixels and constraints evaluating the properties of each noise. We then develop an algorithm based on a primal-dual splitting method to efficiently solve this problem. Experimental results show that the proposed method is much more robust to mixed noise than existing HS anomaly detection methods. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Hyperspectral Anomaly Detection / Component Decomposition / Convex Optimization / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 384, SIP2021-98, pp. 44-49, March 2022. | |
| Paper # | SIP2021-98 | |
| Date of Issue | 2022-02-22 (EA, SIP, SP) | |
| 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) | |
| Download PDF | EA2021-71 SIP2021-98 SP2021-56 |
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| Conference Information | |
| Committee | EA SIP SP IPSJ-SLP |
| Conference Date | 2022-03-01 - 2022-03-02 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SIP |
| Conference Code | 2022-03-EA-SIP-SP-SLP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Robust Hyperspectral Anomaly Detection via Component Decomposition Based on Convex Optimization |
| Sub Title (in English) | |
| Keyword(1) | Hyperspectral Anomaly Detection |
| Keyword(2) | Component Decomposition |
| Keyword(3) | Convex Optimization |
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| 1st Author's Name | Koyo Sato |
| 1st Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 2nd Author's Name | Shunsuke Ono |
| 2nd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
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| Speaker | Author-1 |
| Date Time | 2022-03-01 09:20:00 |
| Presentation Time | 120 minutes |
| Registration for | SIP |
| Paper # | EA2021-71, SIP2021-98, SP2021-56 |
| Volume (vol) | vol.121 |
| Number (no) | no.383(EA), no.384(SIP), no.385(SP) |
| Page | pp.44-49 |
| #Pages | 6 |
| Date of Issue | 2022-02-22 (EA, SIP, SP) |
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