Matris ayriştirma ile hiperspektral verilerde anomali tespiti

Fatma Kucuk; Behcet Ugur Toreyin; Fatih Vehbi Celebi

doi:10.1109/SIU.2018.8404658

Matris ayriştirma ile hiperspektral verilerde anomali tespiti

Translated title of the contribution: Anomaly detection in hyperspectral data with matrix decomposition

Fatma Kucuk, Behcet Ugur Toreyin, Fatih Vehbi Celebi

Informatics Institute

Yildirim Beyazit Universitesi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The role of anomaly detection in hyperspectral imaging is increasingly important. Traditional anomaly detection methods mainly extract information from background images. They use this information to find the difference between anomalies and background. Using generally background information for detecting anomalies and modeling background can cause background contamination with anomaly pixels. However, Low - Rank and Sparse Matrix Decomposition (LRaSMD) based methods can solve this problem due to using both background and anomaly information. In this study, an LRaSMD based anomaly detection method is adopted. According to the experimental results, the proposed method shows better performance than other state-of-art methods.

Translated title of the contribution	Anomaly detection in hyperspectral data with matrix decomposition
Original language	Turkish
Title of host publication	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538615010
DOIs	https://doi.org/10.1109/SIU.2018.8404658
Publication status	Published - 5 Jul 2018
Event	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Duration: 2 May 2018 → 5 May 2018

Publication series

Name	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/Territory	Turkey
City	Izmir
Period	2/05/18 → 5/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

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10.1109/SIU.2018.8404658

Cite this

Kucuk, F., Toreyin, B. U., & Celebi, F. V. (2018). Matris ayriştirma ile hiperspektral verilerde anomali tespiti. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404658

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title = "Matris ayri{\c s}tirma ile hiperspektral verilerde anomali tespiti",

abstract = "The role of anomaly detection in hyperspectral imaging is increasingly important. Traditional anomaly detection methods mainly extract information from background images. They use this information to find the difference between anomalies and background. Using generally background information for detecting anomalies and modeling background can cause background contamination with anomaly pixels. However, Low - Rank and Sparse Matrix Decomposition (LRaSMD) based methods can solve this problem due to using both background and anomaly information. In this study, an LRaSMD based anomaly detection method is adopted. According to the experimental results, the proposed method shows better performance than other state-of-art methods.",

keywords = "Anomaly detection, Hyperspectral imagery, Low-rank, Sparse",

author = "Fatma Kucuk and Toreyin, \{Behcet Ugur\} and Celebi, \{Fatih Vehbi\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 ; Conference date: 02-05-2018 Through 05-05-2018",

year = "2018",

month = jul,

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doi = "10.1109/SIU.2018.8404658",

language = "T{\"u}rk{\c c}e",

series = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--4",

booktitle = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

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Kucuk, F, Toreyin, BU & Celebi, FV 2018, Matris ayriştirma ile hiperspektral verilerde anomali tespiti. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey, 2/05/18. https://doi.org/10.1109/SIU.2018.8404658

Matris ayriştirma ile hiperspektral verilerde anomali tespiti. / Kucuk, Fatma; Toreyin, Behcet Ugur; Celebi, Fatih Vehbi.
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Matris ayriştirma ile hiperspektral verilerde anomali tespiti

AU - Kucuk, Fatma

AU - Toreyin, Behcet Ugur

AU - Celebi, Fatih Vehbi

PY - 2018/7/5

Y1 - 2018/7/5

N2 - The role of anomaly detection in hyperspectral imaging is increasingly important. Traditional anomaly detection methods mainly extract information from background images. They use this information to find the difference between anomalies and background. Using generally background information for detecting anomalies and modeling background can cause background contamination with anomaly pixels. However, Low - Rank and Sparse Matrix Decomposition (LRaSMD) based methods can solve this problem due to using both background and anomaly information. In this study, an LRaSMD based anomaly detection method is adopted. According to the experimental results, the proposed method shows better performance than other state-of-art methods.

AB - The role of anomaly detection in hyperspectral imaging is increasingly important. Traditional anomaly detection methods mainly extract information from background images. They use this information to find the difference between anomalies and background. Using generally background information for detecting anomalies and modeling background can cause background contamination with anomaly pixels. However, Low - Rank and Sparse Matrix Decomposition (LRaSMD) based methods can solve this problem due to using both background and anomaly information. In this study, an LRaSMD based anomaly detection method is adopted. According to the experimental results, the proposed method shows better performance than other state-of-art methods.

KW - Anomaly detection

KW - Hyperspectral imagery

KW - Low-rank

KW - Sparse

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DO - 10.1109/SIU.2018.8404658

M3 - Konferans katkısı

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T3 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

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BT - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Y2 - 2 May 2018 through 5 May 2018

ER -

Matris ayriştirma ile hiperspektral verilerde anomali tespiti

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