Weighted Blind ℓq Hyperspectral Unmixing

Jakob Sigurdsson; Magnus O. Ulfarsson; Johannes R. Sveinsson

doi:10.1109/IGARSS.2019.8898512

Weighted Blind ℓ_q Hyperspectral Unmixing

Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson

Faculty of Electrical and Computer Engineering

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

Abstract

Blind hyperspectral unmixing is the process of decomposing hyperspectral images (HSIs) into pure material spectra (endmembers) and abundances. In this paper, we examine scaling the pixels of the HSI inversely proportional to their ℓ₂ norm, controlled with a tuning parameter. We promote sparse abundances using an ℓ_q penalty and softly enforce the abundance sum constraint using matrix augmentation. The minimization problem is solved using a variant of sparse nonnegative matrix factorization (NMF) and all tuning parameters are selected using Bayesian optimization. The proposed method is evaluated using two real hyperspectral images.

Original language	English
Title of host publication	2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	290-293
Number of pages	4
ISBN (Electronic)	9781538691540
DOIs	https://doi.org/10.1109/IGARSS.2019.8898512
Publication status	Published - Jul 2019
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Other keywords

Bayesian optimization
Hyperspectral unmixing
ℓ regularization

Access to Document

10.1109/IGARSS.2019.8898512

Cite this

Sigurdsson, J., Ulfarsson, M. O., & Sveinsson, J. R. (2019). Weighted Blind ℓ_q Hyperspectral Unmixing. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings (pp. 290-293). Article 8898512 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2019.8898512

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keywords = "Bayesian optimization, Hyperspectral unmixing, ℓ regularization",

author = "Jakob Sigurdsson and Ulfarsson, {Magnus O.} and Sveinsson, {Johannes R.}",

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year = "2019",

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Sigurdsson, J , Ulfarsson, MO & Sveinsson, JR 2019, Weighted Blind ℓ_q Hyperspectral Unmixing. in 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings., 8898512, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 290-293, 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 28/07/19. https://doi.org/10.1109/IGARSS.2019.8898512

Weighted Blind ℓ_q Hyperspectral Unmixing. / Sigurdsson, Jakob ; Ulfarsson, Magnus O.; Sveinsson, Johannes R.
2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 290-293 8898512 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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

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