3D total variation hyperspectral compressive sensing using unmixing

Lei Zhang; Yanning Zhang; Wei Wei; Fei Li

doi:10.1109/IGARSS.2014.6947098

3D total variation hyperspectral compressive sensing using unmixing

Lei Zhang, Yanning Zhang, Wei Wei, Fei Li

School of Computer Science

Northwestern Polytechnical University Xian

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

6 Scopus citations

Abstract

To reduce the huge resource consumption in the hyperspec-tral imaging and transmission, this paper proposes a highperformance compression method. Specially, a novel 3D total variation prior is imposed on abundance fractions of end-members. In this method, compressed data is obtained by a random observation matrix in a compressive sensing way. Based on the hyperspectral linear mixed model and known endmembers, abundance fractions are estimated by an augmented Lagrangian method with the devised prior and then the original data is reconstructed. Extensive experimental results demonstrate the superiority of the proposed method to several state-of-art methods.

Original language	English
Title of host publication	International Geoscience and Remote Sensing Symposium (IGARSS)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2961-2964
Number of pages	4
ISBN (Electronic)	9781479957750
DOIs	https://doi.org/10.1109/IGARSS.2014.6947098
State	Published - 4 Nov 2014
Event	Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 - Quebec City, Canada Duration: 13 Jul 2014 → 18 Jul 2014

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
Country/Territory	Canada
City	Quebec City
Period	13/07/14 → 18/07/14

Keywords

3D Total Variation Prior
Hyperspectral Compressive Sensing
Hyperspectral Linear Unmixing

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10.1109/IGARSS.2014.6947098

Cite this

Zhang, L., Zhang, Y., Wei, W., & Li, F. (2014). 3D total variation hyperspectral compressive sensing using unmixing. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 2961-2964). Article 6947098 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2014.6947098

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abstract = "To reduce the huge resource consumption in the hyperspec-tral imaging and transmission, this paper proposes a highperformance compression method. Specially, a novel 3D total variation prior is imposed on abundance fractions of end-members. In this method, compressed data is obtained by a random observation matrix in a compressive sensing way. Based on the hyperspectral linear mixed model and known endmembers, abundance fractions are estimated by an augmented Lagrangian method with the devised prior and then the original data is reconstructed. Extensive experimental results demonstrate the superiority of the proposed method to several state-of-art methods.",

keywords = "3D Total Variation Prior, Hyperspectral Compressive Sensing, Hyperspectral Linear Unmixing",

author = "Lei Zhang and Yanning Zhang and Wei Wei and Fei Li",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 ; Conference date: 13-07-2014 Through 18-07-2014",

year = "2014",

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doi = "10.1109/IGARSS.2014.6947098",

language = "英语",

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

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

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Zhang, L , Zhang, Y , Wei, W & Li, F 2014, 3D total variation hyperspectral compressive sensing using unmixing. in International Geoscience and Remote Sensing Symposium (IGARSS)., 6947098, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 2961-2964, Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014, Quebec City, Canada, 13/07/14. https://doi.org/10.1109/IGARSS.2014.6947098

3D total variation hyperspectral compressive sensing using unmixing. / Zhang, Lei ; Zhang, Yanning ; Wei, Wei et al.
International Geoscience and Remote Sensing Symposium (IGARSS). Institute of Electrical and Electronics Engineers Inc., 2014. p. 2961-2964 6947098 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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

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AU - Zhang, Yanning

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N2 - To reduce the huge resource consumption in the hyperspec-tral imaging and transmission, this paper proposes a highperformance compression method. Specially, a novel 3D total variation prior is imposed on abundance fractions of end-members. In this method, compressed data is obtained by a random observation matrix in a compressive sensing way. Based on the hyperspectral linear mixed model and known endmembers, abundance fractions are estimated by an augmented Lagrangian method with the devised prior and then the original data is reconstructed. Extensive experimental results demonstrate the superiority of the proposed method to several state-of-art methods.

AB - To reduce the huge resource consumption in the hyperspec-tral imaging and transmission, this paper proposes a highperformance compression method. Specially, a novel 3D total variation prior is imposed on abundance fractions of end-members. In this method, compressed data is obtained by a random observation matrix in a compressive sensing way. Based on the hyperspectral linear mixed model and known endmembers, abundance fractions are estimated by an augmented Lagrangian method with the devised prior and then the original data is reconstructed. Extensive experimental results demonstrate the superiority of the proposed method to several state-of-art methods.

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3D total variation hyperspectral compressive sensing using unmixing

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