Total variation based hyperspectral feature extraction

Behnood Rasti; Johannes R. Sveinsson; Magnus O. Ulfarsson

doi:10.1109/IGARSS.2014.6947528

Total variation based hyperspectral feature extraction

Behnood Rasti, Johannes R. Sveinsson, Magnus O. Ulfarsson

Faculty of Electrical and Computer Engineering

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

10 Citations (Scopus)

Abstract

In this paper, a hyperspectral feature extraction method is proposed. A low-rank linear model using the right eigenvector of the observed data is given for hyperspectral images. A total variation (TV) based regularization called Low-Rank TV regularization (LRTV) is used for hyperspectral feature extraction. The feature extraction is used for hyperspectral image classification. The classification accuracies obtained are significantly better than the ones obtained using features extracted by Principal Component Analysis (PCA) and Maximum Noise Fraction (MNF).

Original language	English
Title of host publication	International Geoscience and Remote Sensing Symposium (IGARSS)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4644-4647
Number of pages	4
ISBN (Electronic)	9781479957750
DOIs	https://doi.org/10.1109/IGARSS.2014.6947528
Publication status	Published - 4 Nov 2014
Event	Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 - Quebec City, Canada Duration: 13 Jul 2014 → 18 Jul 2014

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
Country/Territory	Canada
City	Quebec City
Period	13/07/14 → 18/07/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Other keywords

Feature extraction
hyperspectral image
low-rank model
regularization
total variation

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10.1109/IGARSS.2014.6947528

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Rasti, B., Sveinsson, J. R., & Ulfarsson, M. O. (2014). Total variation based hyperspectral feature extraction. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 4644-4647). Article 6947528 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2014.6947528

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title = "Total variation based hyperspectral feature extraction",

abstract = "In this paper, a hyperspectral feature extraction method is proposed. A low-rank linear model using the right eigenvector of the observed data is given for hyperspectral images. A total variation (TV) based regularization called Low-Rank TV regularization (LRTV) is used for hyperspectral feature extraction. The feature extraction is used for hyperspectral image classification. The classification accuracies obtained are significantly better than the ones obtained using features extracted by Principal Component Analysis (PCA) and Maximum Noise Fraction (MNF).",

keywords = "Feature extraction, hyperspectral image, low-rank model, regularization, total variation",

author = "Behnood Rasti and Sveinsson, {Johannes R.} and Ulfarsson, {Magnus O.}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 ; Conference date: 13-07-2014 Through 18-07-2014",

year = "2014",

month = nov,

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doi = "10.1109/IGARSS.2014.6947528",

language = "English",

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

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

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Rasti, B, Sveinsson, JR & Ulfarsson, MO 2014, Total variation based hyperspectral feature extraction. in International Geoscience and Remote Sensing Symposium (IGARSS)., 6947528, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 4644-4647, Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014, Quebec City, Canada, 13/07/14. https://doi.org/10.1109/IGARSS.2014.6947528

Total variation based hyperspectral feature extraction. / Rasti, Behnood; Sveinsson, Johannes R.; Ulfarsson, Magnus O.
International Geoscience and Remote Sensing Symposium (IGARSS). Institute of Electrical and Electronics Engineers Inc., 2014. p. 4644-4647 6947528 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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

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T1 - Total variation based hyperspectral feature extraction

AU - Rasti, Behnood

AU - Sveinsson, Johannes R.

AU - Ulfarsson, Magnus O.

PY - 2014/11/4

Y1 - 2014/11/4

N2 - In this paper, a hyperspectral feature extraction method is proposed. A low-rank linear model using the right eigenvector of the observed data is given for hyperspectral images. A total variation (TV) based regularization called Low-Rank TV regularization (LRTV) is used for hyperspectral feature extraction. The feature extraction is used for hyperspectral image classification. The classification accuracies obtained are significantly better than the ones obtained using features extracted by Principal Component Analysis (PCA) and Maximum Noise Fraction (MNF).

AB - In this paper, a hyperspectral feature extraction method is proposed. A low-rank linear model using the right eigenvector of the observed data is given for hyperspectral images. A total variation (TV) based regularization called Low-Rank TV regularization (LRTV) is used for hyperspectral feature extraction. The feature extraction is used for hyperspectral image classification. The classification accuracies obtained are significantly better than the ones obtained using features extracted by Principal Component Analysis (PCA) and Maximum Noise Fraction (MNF).

KW - Feature extraction

KW - hyperspectral image

KW - low-rank model

KW - regularization

KW - total variation

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U2 - 10.1109/IGARSS.2014.6947528

DO - 10.1109/IGARSS.2014.6947528

M3 - Conference contribution

AN - SCOPUS:84911373788

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 4644

EP - 4647

BT - International Geoscience and Remote Sensing Symposium (IGARSS)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014

Y2 - 13 July 2014 through 18 July 2014

ER -

Total variation based hyperspectral feature extraction

Abstract

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Bibliographical note

Other keywords

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