Learning a low-coherence dictionary to address spectral variability for hyperspectral unmixing

Danfeng Hong; Naoto Yokoya; Jocelyn Chanussot; Xiao Xiang Zhu

doi:10.1109/ICIP.2017.8296278

Learning a low-coherence dictionary to address spectral variability for hyperspectral unmixing

Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu

Chair of Data Science in Earth Observation

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

19 Scopus citations

Abstract

This paper presents a novel spectral mixture model to address spectral variability in inverse problems of hyperspectral unmixing. Based on the linear mixture model (LMM), our model introduces a spectral variability dictionary to account for any residuals that cannot be explained by the LMM. Atoms in the dictionary are assumed to be low-coherent with spectral signatures of endmembers. A dictionary learning technique is proposed to learn the spectral variability dictionary while solving unmixing problems simultaneously. Experimental results on synthetic and real datasets demonstrate that the performance of the proposed method is superior to state-of-the-art methods.

Original language	English
Title of host publication	2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
Publisher	IEEE Computer Society
Pages	235-239
Number of pages	5
ISBN (Electronic)	9781509021758
DOIs	https://doi.org/10.1109/ICIP.2017.8296278
State	Published - 2 Jul 2017
Event	24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China Duration: 17 Sep 2017 → 20 Sep 2017

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2017-September
ISSN (Print)	1522-4880

Conference

Conference	24th IEEE International Conference on Image Processing, ICIP 2017
Country/Territory	China
City	Beijing
Period	17/09/17 → 20/09/17

Keywords

Alternating direction method of multipliers
Low-coherent dictionary learning
Remote sensing
Spectral unmixing
Spectral variability

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10.1109/ICIP.2017.8296278

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Hong, D., Yokoya, N., Chanussot, J., & Zhu, X. X. (2017). Learning a low-coherence dictionary to address spectral variability for hyperspectral unmixing. In 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings (pp. 235-239). (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2017.8296278

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title = "Learning a low-coherence dictionary to address spectral variability for hyperspectral unmixing",

abstract = "This paper presents a novel spectral mixture model to address spectral variability in inverse problems of hyperspectral unmixing. Based on the linear mixture model (LMM), our model introduces a spectral variability dictionary to account for any residuals that cannot be explained by the LMM. Atoms in the dictionary are assumed to be low-coherent with spectral signatures of endmembers. A dictionary learning technique is proposed to learn the spectral variability dictionary while solving unmixing problems simultaneously. Experimental results on synthetic and real datasets demonstrate that the performance of the proposed method is superior to state-of-the-art methods.",

keywords = "Alternating direction method of multipliers, Low-coherent dictionary learning, Remote sensing, Spectral unmixing, Spectral variability",

author = "Danfeng Hong and Naoto Yokoya and Jocelyn Chanussot and Zhu, {Xiao Xiang}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Conference on Image Processing, ICIP 2017 ; Conference date: 17-09-2017 Through 20-09-2017",

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doi = "10.1109/ICIP.2017.8296278",

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Hong, D, Yokoya, N, Chanussot, J & Zhu, XX 2017, Learning a low-coherence dictionary to address spectral variability for hyperspectral unmixing. in 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. Proceedings - International Conference on Image Processing, ICIP, vol. 2017-September, IEEE Computer Society, pp. 235-239, 24th IEEE International Conference on Image Processing, ICIP 2017, Beijing, China, 17/09/17. https://doi.org/10.1109/ICIP.2017.8296278

Learning a low-coherence dictionary to address spectral variability for hyperspectral unmixing. / Hong, Danfeng; Yokoya, Naoto; Chanussot, Jocelyn et al.
2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE Computer Society, 2017. p. 235-239 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September).

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

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AU - Zhu, Xiao Xiang

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N2 - This paper presents a novel spectral mixture model to address spectral variability in inverse problems of hyperspectral unmixing. Based on the linear mixture model (LMM), our model introduces a spectral variability dictionary to account for any residuals that cannot be explained by the LMM. Atoms in the dictionary are assumed to be low-coherent with spectral signatures of endmembers. A dictionary learning technique is proposed to learn the spectral variability dictionary while solving unmixing problems simultaneously. Experimental results on synthetic and real datasets demonstrate that the performance of the proposed method is superior to state-of-the-art methods.

AB - This paper presents a novel spectral mixture model to address spectral variability in inverse problems of hyperspectral unmixing. Based on the linear mixture model (LMM), our model introduces a spectral variability dictionary to account for any residuals that cannot be explained by the LMM. Atoms in the dictionary are assumed to be low-coherent with spectral signatures of endmembers. A dictionary learning technique is proposed to learn the spectral variability dictionary while solving unmixing problems simultaneously. Experimental results on synthetic and real datasets demonstrate that the performance of the proposed method is superior to state-of-the-art methods.

KW - Alternating direction method of multipliers

KW - Low-coherent dictionary learning

KW - Remote sensing

KW - Spectral unmixing

KW - Spectral variability

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M3 - Conference contribution

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Learning a low-coherence dictionary to address spectral variability for hyperspectral unmixing

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