Unsupervised nearest regularized subspace for anomaly detection in hyperspectral imagery

Wei Li; Qian Du

doi:10.1109/IGARSS.2013.6721345

Unsupervised nearest regularized subspace for anomaly detection in hyperspectral imagery

Wei Li, Qian Du

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

18 Citations (Scopus)

Abstract

A method of unsupervised nearest regularized subspace is proposed for anomaly detection in hyperspectral imagery. Based on a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination, for which the weight vector is calculated by distance-weighted Tikhonov regularization. Proposed detector returns the similarity measurement between the testing pixel and its approximation. Experimental results for real hyperspectral data of proposed approach are demonstrated and compared to other traditional detection techniques.

Original language	English
Title of host publication	2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
Pages	1055-1058
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS.2013.6721345
Publication status	Published - 2013
Externally published	Yes
Event	2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Melbourne, VIC, Australia Duration: 21 Jul 2013 → 26 Jul 2013

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
Country/Territory	Australia
City	Melbourne, VIC
Period	21/07/13 → 26/07/13

Keywords

Anomaly Detection
Hyperspectral Imagery
Tikhonov regularization

Access to Document

10.1109/IGARSS.2013.6721345

Cite this

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title = "Unsupervised nearest regularized subspace for anomaly detection in hyperspectral imagery",

abstract = "A method of unsupervised nearest regularized subspace is proposed for anomaly detection in hyperspectral imagery. Based on a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination, for which the weight vector is calculated by distance-weighted Tikhonov regularization. Proposed detector returns the similarity measurement between the testing pixel and its approximation. Experimental results for real hyperspectral data of proposed approach are demonstrated and compared to other traditional detection techniques.",

keywords = "Anomaly Detection, Hyperspectral Imagery, Tikhonov regularization",

author = "Wei Li and Qian Du",

year = "2013",

doi = "10.1109/IGARSS.2013.6721345",

language = "English",

isbn = "9781479911141",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

pages = "1055--1058",

booktitle = "2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings",

note = "2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 ; Conference date: 21-07-2013 Through 26-07-2013",

}

Li, W & Du, Q 2013, Unsupervised nearest regularized subspace for anomaly detection in hyperspectral imagery. in 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings., 6721345, International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1055-1058, 2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013, Melbourne, VIC, Australia, 21/07/13. https://doi.org/10.1109/IGARSS.2013.6721345

Unsupervised nearest regularized subspace for anomaly detection in hyperspectral imagery. / Li, Wei; Du, Qian.
2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings. 2013. p. 1055-1058 6721345 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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

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AU - Du, Qian

PY - 2013

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AB - A method of unsupervised nearest regularized subspace is proposed for anomaly detection in hyperspectral imagery. Based on a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination, for which the weight vector is calculated by distance-weighted Tikhonov regularization. Proposed detector returns the similarity measurement between the testing pixel and its approximation. Experimental results for real hyperspectral data of proposed approach are demonstrated and compared to other traditional detection techniques.

KW - Anomaly Detection

KW - Hyperspectral Imagery

KW - Tikhonov regularization

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DO - 10.1109/IGARSS.2013.6721345

M3 - Conference contribution

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T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

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BT - 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings

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Y2 - 21 July 2013 through 26 July 2013

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Unsupervised nearest regularized subspace for anomaly detection in hyperspectral imagery

Abstract

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Keywords

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