Total variation and ℓq based hyperspectral unmixing for feature extraction and classification

Jakob Sigurdsson; Magnus O. Ulfarsson; Johannes R. Sveinsson

doi:10.1109/IGARSS.2015.7325794

Total variation and ℓ_q based hyperspectral unmixing for feature extraction and classification

Jakob Sigurdsson
, Magnus O. Ulfarsson
, Johannes R. Sveinsson

Faculty of Electrical and Computer Engineering

University of Iceland

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

Abstract

Blind hyperspectral unmixing jointly estimates both the endmembers and the abundances of hyperspectral images. The endmembers represent the spectral signatures of material found in the image and the abundances specify the amount of each material seen in each pixel in the image. In this paper, a blind hyperspectral unmixing method for feature extraction and classification using total variation (TV) and ℓ_q sparse regularization is proposed. The abundances found are used as features for classification. The classification results are compared to results obtained using Principal Component analysis (PCA) and also to results obtained using hyperspectral unmixing using only TV and sparsity, respectively.

Original language	English
Title of host publication	2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	437-440
Number of pages	4
ISBN (Electronic)	9781479979295
ISBN (Print)	9781479979295
DOIs	https://doi.org/10.1109/IGARSS.2015.7325794
Publication status	Published - 10 Nov 2015
Event	IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy Duration: 26 Jul 2015 → 31 Jul 2015

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2015-November

Conference

Conference	IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Country/Territory	Italy
City	Milan
Period	26/07/15 → 31/07/15

Bibliographical note

Other keywords

Hyperspectral unmixing
blind signal separation
classification
dyadic cyclic descent
feature extraction
linear unmixing
total variation

Access to Document

10.1109/IGARSS.2015.7325794

Cite this

Sigurdsson, J., Ulfarsson, M. O., & Sveinsson, J. R. (2015). Total variation and ℓ_q based hyperspectral unmixing for feature extraction and classification. In 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings (pp. 437-440). Article 7325794 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2015.7325794

Sigurdsson, Jakob ; Ulfarsson, Magnus O. ; Sveinsson, Johannes R. / Total variation and ℓ_q based hyperspectral unmixing for feature extraction and classification. 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 437-440 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "Total variation and ℓq based hyperspectral unmixing for feature extraction and classification",

abstract = "Blind hyperspectral unmixing jointly estimates both the endmembers and the abundances of hyperspectral images. The endmembers represent the spectral signatures of material found in the image and the abundances specify the amount of each material seen in each pixel in the image. In this paper, a blind hyperspectral unmixing method for feature extraction and classification using total variation (TV) and ℓq sparse regularization is proposed. The abundances found are used as features for classification. The classification results are compared to results obtained using Principal Component analysis (PCA) and also to results obtained using hyperspectral unmixing using only TV and sparsity, respectively.",

keywords = "Hyperspectral unmixing, blind signal separation, classification, dyadic cyclic descent, feature extraction, linear unmixing, total variation",

author = "Jakob Sigurdsson and Ulfarsson, \{Magnus O.\} and Sveinsson, \{Johannes R.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 ; Conference date: 26-07-2015 Through 31-07-2015",

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doi = "10.1109/IGARSS.2015.7325794",

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

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Sigurdsson, J , Ulfarsson, MO & Sveinsson, JR 2015, Total variation and ℓ_q based hyperspectral unmixing for feature extraction and classification. in 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings., 7325794, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2015-November, Institute of Electrical and Electronics Engineers Inc., pp. 437-440, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015, Milan, Italy, 26/07/15. https://doi.org/10.1109/IGARSS.2015.7325794

Total variation and ℓ_q based hyperspectral unmixing for feature extraction and classification. / Sigurdsson, Jakob ; Ulfarsson, Magnus O.; Sveinsson, Johannes R.
2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 437-440 7325794 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2015-November).

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

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AB - Blind hyperspectral unmixing jointly estimates both the endmembers and the abundances of hyperspectral images. The endmembers represent the spectral signatures of material found in the image and the abundances specify the amount of each material seen in each pixel in the image. In this paper, a blind hyperspectral unmixing method for feature extraction and classification using total variation (TV) and ℓq sparse regularization is proposed. The abundances found are used as features for classification. The classification results are compared to results obtained using Principal Component analysis (PCA) and also to results obtained using hyperspectral unmixing using only TV and sparsity, respectively.

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KW - blind signal separation

KW - classification

KW - dyadic cyclic descent

KW - feature extraction

KW - linear unmixing

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SN - 9781479979295

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BT - 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings

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Sigurdsson J , Ulfarsson MO, Sveinsson JR. Total variation and ℓ_q based hyperspectral unmixing for feature extraction and classification. In 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 437-440. 7325794. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2015.7325794