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 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.
Original language | English |
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Title of host publication | 2019 10th Workshop on Hyperspectral Imaging and Signal Processing |
Subtitle of host publication | Evolution in Remote Sensing, WHISPERS 2019 |
ISBN (Electronic) | 9781728152943 |
DOIs | |
State | Published - Sep 2019 |
Event | 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019 - Amsterdam, Netherlands Duration: Sep 24 2019 → Sep 26 2019 |
Publication series
Name | Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing |
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Volume | 2019-September |
ISSN (Print) | 2158-6276 |
Conference
Conference | 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019 |
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Country/Territory | Netherlands |
City | Amsterdam |
Period | 9/24/19 → 9/26/19 |
Bibliographical note
Funding Information: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
Funding Information:
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
Publisher Copyright:
© 2019 IEEE.
Keywords
- Hyperspectral imaging
- Nystrom method
- alternating direction method of multipliers
- graph Laplacian
- hyperspectral unmixing
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing