Fast Blind Hyperspectral Unmixing Based on Graph Laplacian

Jing Qin; Harlin Lee; Jocelyn T. Chi; Yifei Lou; Jocelyn Chanussot; Andrea L. Bertozzi

doi:10.1109/WHISPERS.2019.8921375

Fast Blind Hyperspectral Unmixing Based on Graph Laplacian

Jing Qin
, Harlin Lee
, Jocelyn T. Chi
, Yifei Lou
, Jocelyn Chanussot
, Andrea L. Bertozzi

Producción científica: Conference contribution › revisión exhaustiva

10 Citas (Scopus)

Resumen

Blind hyperspectral unmixing is a challenging problem in remote sensing, which aims to infer material spectra and abundances from the given hyperspectral data. Many traditional methods suffer from poor identification of materials and/or expensive computational costs, which can be partially eased by trading the accuracy with efficiency. In this work, we propose a fast graph-based blind unmixing approach. In particular, we apply the Nyström method to efficiently approximate eigenvalues and eigenvectors of a matrix corresponding to a normalized graph Laplacian. Then the alternating direction method of multipliers (ADMM) yields a fast numerical algorithm. Experiments on a real dataset illustrate great potential of the proposed method in terms of accuracy and efficiency.

Idioma original	English
Título de la publicación alojada	2019 10th Workshop on Hyperspectral Imaging and Signal Processing
Subtítulo de la publicación alojada	Evolution in Remote Sensing, WHISPERS 2019
ISBN (versión digital)	9781728152943
DOI	https://doi.org/10.1109/WHISPERS.2019.8921375
Estado	Published - sept 2019
Evento	10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019 - Amsterdam, Netherlands Duración: sept 24 2019 → sept 26 2019

Serie de la publicación

Nombre	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volumen	2019-September
ISSN (versión impresa)	2158-6276

Conference

Conference	10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019
País/Territorio	Netherlands
Ciudad	Amsterdam
Período	9/24/19 → 9/26/19

Nota bibliográfica

Publisher Copyright:
© 2019 IEEE.

Financiación

The initial research for this effort was conducted at the Research Collaboration Workshop for Women in Data Science and Mathematics that was held at ICERM, July 29-August 2, 2019. Funding for the workshop was provided by ICERM. In addition, Qin is supported by the NSF grant DMS-1941197, Lou by the NSF CAREER grant DMS-1846690, Lee by the ONR grant N00014-19-1-2404, Chi by the NSF grant DMS-1760374, Chaussot by the Grant ANR-16 ASTR-0027-01, and Bertozzi by the DARPA grant FA8750-18-2-0066

Financiadores	Número del financiador
NSF CAREER
ICERM
National Sleep Foundation
Office of Naval Research Naval Academy	N00014-19-1-2404, ANR-16 ASTR-0027-01, DMS-1760374
National Science Foundation Arctic Social Science Program	1846690, DMS-1941197, DMS-1846690, 1941197
Defense Advanced Research Projects Agency	FA8750-18-2-0066

ASJC Scopus subject areas

Signal Processing
Computer Vision and Pattern Recognition

Acceder al documento

10.1109/WHISPERS.2019.8921375

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Qin, J., Lee, H., Chi, J. T., Lou, Y., Chanussot, J., & Bertozzi, A. L. (2019). Fast Blind Hyperspectral Unmixing Based on Graph Laplacian. En 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019 Artículo 8921375 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2019-September). https://doi.org/10.1109/WHISPERS.2019.8921375

@inproceedings{2d17ba7794944d50b4e34c4728f30499,

title = "Fast Blind Hyperspectral Unmixing Based on Graph Laplacian",

abstract = "Blind hyperspectral unmixing is a challenging problem in remote sensing, which aims to infer material spectra and abundances from the given hyperspectral data. Many traditional methods suffer from poor identification of materials and/or expensive computational costs, which can be partially eased by trading the accuracy with efficiency. In this work, we propose a fast graph-based blind unmixing approach. In particular, we apply the Nystr{\"o}m method to efficiently approximate eigenvalues and eigenvectors of a matrix corresponding to a normalized graph Laplacian. Then the alternating direction method of multipliers (ADMM) yields a fast numerical algorithm. Experiments on a real dataset illustrate great potential of the proposed method in terms of accuracy and efficiency.",

keywords = "Hyperspectral imaging, Nystrom method, alternating direction method of multipliers, graph Laplacian, hyperspectral unmixing",

author = "Jing Qin and Harlin Lee and Chi, \{Jocelyn T.\} and Yifei Lou and Jocelyn Chanussot and Bertozzi, \{Andrea L.\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019 ; Conference date: 24-09-2019 Through 26-09-2019",

year = "2019",

month = sep,

doi = "10.1109/WHISPERS.2019.8921375",

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Qin, J, Lee, H, Chi, JT, Lou, Y, Chanussot, J & Bertozzi, AL 2019, Fast Blind Hyperspectral Unmixing Based on Graph Laplacian. En 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019., 8921375, Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, vol. 2019-September, 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019, Amsterdam, Netherlands, 9/24/19. https://doi.org/10.1109/WHISPERS.2019.8921375

Fast Blind Hyperspectral Unmixing Based on Graph Laplacian. / Qin, Jing; Lee, Harlin; Chi, Jocelyn T. et al.
2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019. 2019. 8921375 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2019-September).

Producción científica: Conference contribution › revisión exhaustiva

TY - GEN

T1 - Fast Blind Hyperspectral Unmixing Based on Graph Laplacian

AU - Qin, Jing

AU - Lee, Harlin

AU - Chi, Jocelyn T.

AU - Lou, Yifei

AU - Chanussot, Jocelyn

AU - Bertozzi, Andrea L.

PY - 2019/9

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AB - Blind hyperspectral unmixing is a challenging problem in remote sensing, which aims to infer material spectra and abundances from the given hyperspectral data. Many traditional methods suffer from poor identification of materials and/or expensive computational costs, which can be partially eased by trading the accuracy with efficiency. In this work, we propose a fast graph-based blind unmixing approach. In particular, we apply the Nyström method to efficiently approximate eigenvalues and eigenvectors of a matrix corresponding to a normalized graph Laplacian. Then the alternating direction method of multipliers (ADMM) yields a fast numerical algorithm. Experiments on a real dataset illustrate great potential of the proposed method in terms of accuracy and efficiency.

KW - Hyperspectral imaging

KW - Nystrom method

KW - alternating direction method of multipliers

KW - graph Laplacian

KW - hyperspectral unmixing

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Fast Blind Hyperspectral Unmixing Based on Graph Laplacian

Resumen

Serie de la publicación

Conference

Nota bibliográfica

Financiación

ASJC Scopus subject areas

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