Towards robust vegetation indices: The multi-correlation matrix strategy

Helge Aasen; Martin Leon Gnyp; Yuxin Miao; Georg Bareth

doi:10.1109/WHISPERS.2014.8077547

Towards robust vegetation indices: The multi-correlation matrix strategy

Helge Aasen
, Martin Leon Gnyp
, Yuxin Miao
, Georg Bareth

Soil, Water, and Climate

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

Abstract

Hyperspectral vegetation indices (HVIs) have shown great potential for characterizing and monitoring vegetation and agricultural crops. Additionally, hyperspectral data becomes more commonly available. Latter may be used to address varying annual crop growth. In this paper we describe the multi-correlation matrix strategy as a new approach to derive robust HVIs from multiple hyperspectral field spectrometers datasets. The approach combines the information from multiple correlation matrices (CMs). The software HyperCor is used to automate the data pre-processing and CMs computation. In this study we use data from three growth stages (tillering, stem elongation, heading) in five years (2007-2009, 2011 and 2012) to estimate rice biomass. The new approach is validated through leave-one-out cross-validation and compared to results from a direct approach. On average the multi-correlation matrix approach showed 15% better performance and could reduce the RMSE compared to the direct approach.

Original language	English (US)
Title of host publication	2014 6th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publication	Evolution in Remote Sensing, WHISPERS 2014
Publisher	IEEE Computer Society
ISBN (Electronic)	9781467390125
DOIs	https://doi.org/10.1109/WHISPERS.2014.8077547
State	Published - Jun 28 2014
Event	6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 - Lausanne, Switzerland Duration: Jun 24 2014 → Jun 27 2014

Publication series

Name	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume	2014-June
ISSN (Print)	2158-6276

Other

Other	6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014
Country/Territory	Switzerland
City	Lausanne
Period	6/24/14 → 6/27/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

HyperCor
biomass
hyperspectral data processing
multi-correlation matrix strategy
rice
robust vegetation indices

Publisher link

10.1109/WHISPERS.2014.8077547

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Aasen, H., Gnyp, M. L., Miao, Y., & Bareth, G. (2014). Towards robust vegetation indices: The multi-correlation matrix strategy. In 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 Article 8077547 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2014-June). IEEE Computer Society. https://doi.org/10.1109/WHISPERS.2014.8077547

Towards robust vegetation indices: The multi-correlation matrix strategy. / Aasen, Helge; Gnyp, Martin Leon; Miao, Yuxin et al.
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014. IEEE Computer Society, 2014. 8077547 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2014-June).

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

Aasen, H, Gnyp, ML, Miao, Y & Bareth, G 2014, Towards robust vegetation indices: The multi-correlation matrix strategy. in 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014., 8077547, Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, vol. 2014-June, IEEE Computer Society, 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014, Lausanne, Switzerland, 6/24/14. https://doi.org/10.1109/WHISPERS.2014.8077547

Aasen H, Gnyp ML, Miao Y, Bareth G. Towards robust vegetation indices: The multi-correlation matrix strategy. In 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014. IEEE Computer Society. 2014. 8077547. (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing). doi: 10.1109/WHISPERS.2014.8077547

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abstract = "Hyperspectral vegetation indices (HVIs) have shown great potential for characterizing and monitoring vegetation and agricultural crops. Additionally, hyperspectral data becomes more commonly available. Latter may be used to address varying annual crop growth. In this paper we describe the multi-correlation matrix strategy as a new approach to derive robust HVIs from multiple hyperspectral field spectrometers datasets. The approach combines the information from multiple correlation matrices (CMs). The software HyperCor is used to automate the data pre-processing and CMs computation. In this study we use data from three growth stages (tillering, stem elongation, heading) in five years (2007-2009, 2011 and 2012) to estimate rice biomass. The new approach is validated through leave-one-out cross-validation and compared to results from a direct approach. On average the multi-correlation matrix approach showed 15\% better performance and could reduce the RMSE compared to the direct approach.",

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AU - Bareth, Georg

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N2 - Hyperspectral vegetation indices (HVIs) have shown great potential for characterizing and monitoring vegetation and agricultural crops. Additionally, hyperspectral data becomes more commonly available. Latter may be used to address varying annual crop growth. In this paper we describe the multi-correlation matrix strategy as a new approach to derive robust HVIs from multiple hyperspectral field spectrometers datasets. The approach combines the information from multiple correlation matrices (CMs). The software HyperCor is used to automate the data pre-processing and CMs computation. In this study we use data from three growth stages (tillering, stem elongation, heading) in five years (2007-2009, 2011 and 2012) to estimate rice biomass. The new approach is validated through leave-one-out cross-validation and compared to results from a direct approach. On average the multi-correlation matrix approach showed 15% better performance and could reduce the RMSE compared to the direct approach.

AB - Hyperspectral vegetation indices (HVIs) have shown great potential for characterizing and monitoring vegetation and agricultural crops. Additionally, hyperspectral data becomes more commonly available. Latter may be used to address varying annual crop growth. In this paper we describe the multi-correlation matrix strategy as a new approach to derive robust HVIs from multiple hyperspectral field spectrometers datasets. The approach combines the information from multiple correlation matrices (CMs). The software HyperCor is used to automate the data pre-processing and CMs computation. In this study we use data from three growth stages (tillering, stem elongation, heading) in five years (2007-2009, 2011 and 2012) to estimate rice biomass. The new approach is validated through leave-one-out cross-validation and compared to results from a direct approach. On average the multi-correlation matrix approach showed 15% better performance and could reduce the RMSE compared to the direct approach.

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KW - multi-correlation matrix strategy

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BT - 2014 6th Workshop on Hyperspectral Image and Signal Processing

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ER -

Towards robust vegetation indices: The multi-correlation matrix strategy

Abstract

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UN SDGs

Keywords

Publisher link

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