Sparse distributed hyperspectral unmixing

Jakob Sigurdsson; Magnus O. Ulfarsson; Johannes R. Sveinsson; Jose M. Bioucas-Dias

doi:10.1109/IGARSS.2016.7730824

Sparse distributed hyperspectral unmixing

Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson, Jose M. Bioucas-Dias

Faculty of Electrical and Computer Engineering

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

4 Citations (Scopus)

Abstract

Blind hyperspectral unmixing is the task of jointly estimating the spectral signatures of material in a hyperspectral images and their abundances at each pixel. The size of hyperspectral images are usually very large, which may raise difficulties for classical optimization algorithms, due to limited memory of the hardware used. One solution to this problem is to consider distributed algorithms. In this paper, we develop a distributed sparse hyperspectral unmixing algorithm using the alternating direction method of multipliers (ADMM) algorithm and ℓ₁ sparse regularization. Each sub-problem does not need to have access to the whole hyperspectral image. The algorithm is evaluated using a very large real hyperspectral image.

Original language	English
Title of host publication	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6994-6997
Number of pages	4
ISBN (Electronic)	9781509033324
DOIs	https://doi.org/10.1109/IGARSS.2016.7730824
Publication status	Published - 1 Nov 2016
Event	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China Duration: 10 Jul 2016 → 15 Jul 2016

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2016-November

Conference

Conference	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Country/Territory	China
City	Beijing
Period	10/07/16 → 15/07/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Other keywords

alternating direction method of multipliers
blind signal separation
dyadic cyclic descent
feature extraction
Hyperspectral unmixing
linear unmixing
optimization

Access to Document

10.1109/IGARSS.2016.7730824

Cite this

Sigurdsson, J., Ulfarsson, M. O., Sveinsson, J. R., & Bioucas-Dias, J. M. (2016). Sparse distributed hyperspectral unmixing. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (pp. 6994-6997). Article 7730824 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7730824

@inproceedings{224d366e5eee4f848f5e27426a944ed7,

title = "Sparse distributed hyperspectral unmixing",

abstract = "Blind hyperspectral unmixing is the task of jointly estimating the spectral signatures of material in a hyperspectral images and their abundances at each pixel. The size of hyperspectral images are usually very large, which may raise difficulties for classical optimization algorithms, due to limited memory of the hardware used. One solution to this problem is to consider distributed algorithms. In this paper, we develop a distributed sparse hyperspectral unmixing algorithm using the alternating direction method of multipliers (ADMM) algorithm and ℓ1 sparse regularization. Each sub-problem does not need to have access to the whole hyperspectral image. The algorithm is evaluated using a very large real hyperspectral image.",

keywords = "alternating direction method of multipliers, blind signal separation, dyadic cyclic descent, feature extraction, Hyperspectral unmixing, linear unmixing, optimization",

author = "Jakob Sigurdsson and Ulfarsson, {Magnus O.} and Sveinsson, {Johannes R.} and Bioucas-Dias, {Jose M.}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

year = "2016",

month = nov,

day = "1",

doi = "10.1109/IGARSS.2016.7730824",

language = "English",

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

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

pages = "6994--6997",

booktitle = "2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings",

address = "United States",

}

Sigurdsson, J , Ulfarsson, MO, Sveinsson, JR & Bioucas-Dias, JM 2016, Sparse distributed hyperspectral unmixing. in 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings., 7730824, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2016-November, Institute of Electrical and Electronics Engineers Inc., pp. 6994-6997, 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, China, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7730824

Sparse distributed hyperspectral unmixing. / Sigurdsson, Jakob ; Ulfarsson, Magnus O.; Sveinsson, Johannes R. et al.
2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 6994-6997 7730824 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November).

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

TY - GEN

T1 - Sparse distributed hyperspectral unmixing

AU - Sigurdsson, Jakob

AU - Ulfarsson, Magnus O.

AU - Sveinsson, Johannes R.

AU - Bioucas-Dias, Jose M.

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Blind hyperspectral unmixing is the task of jointly estimating the spectral signatures of material in a hyperspectral images and their abundances at each pixel. The size of hyperspectral images are usually very large, which may raise difficulties for classical optimization algorithms, due to limited memory of the hardware used. One solution to this problem is to consider distributed algorithms. In this paper, we develop a distributed sparse hyperspectral unmixing algorithm using the alternating direction method of multipliers (ADMM) algorithm and ℓ1 sparse regularization. Each sub-problem does not need to have access to the whole hyperspectral image. The algorithm is evaluated using a very large real hyperspectral image.

AB - Blind hyperspectral unmixing is the task of jointly estimating the spectral signatures of material in a hyperspectral images and their abundances at each pixel. The size of hyperspectral images are usually very large, which may raise difficulties for classical optimization algorithms, due to limited memory of the hardware used. One solution to this problem is to consider distributed algorithms. In this paper, we develop a distributed sparse hyperspectral unmixing algorithm using the alternating direction method of multipliers (ADMM) algorithm and ℓ1 sparse regularization. Each sub-problem does not need to have access to the whole hyperspectral image. The algorithm is evaluated using a very large real hyperspectral image.

KW - alternating direction method of multipliers

KW - blind signal separation

KW - dyadic cyclic descent

KW - feature extraction

KW - Hyperspectral unmixing

KW - linear unmixing

KW - optimization

UR - http://www.scopus.com/inward/record.url?scp=85007473741&partnerID=8YFLogxK

U2 - 10.1109/IGARSS.2016.7730824

DO - 10.1109/IGARSS.2016.7730824

M3 - Conference contribution

AN - SCOPUS:85007473741

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 6994

EP - 6997

BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016

Y2 - 10 July 2016 through 15 July 2016

ER -

Sigurdsson J , Ulfarsson MO, Sveinsson JR, Bioucas-Dias JM. Sparse distributed hyperspectral unmixing. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 6994-6997. 7730824. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2016.7730824

Sparse distributed hyperspectral unmixing

Abstract

Publication series

Conference

Bibliographical note

Other keywords

Access to Document

Fingerprint

Cite this