The estimation of high resolution urban surface temperature using hyperspectral spectral mixture analysis

Kai Liu; Hongbo Su; Weimin Wang; Xueke Li

doi:10.1109/WHISPERS.2015.8075498

The estimation of high resolution urban surface temperature using hyperspectral spectral mixture analysis

Kai Liu
, Hongbo Su
, Weimin Wang
, Xueke Li

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

In this paper, a simple method that uses hyperspectral spectral mixture analysis to estimate fine resolution urban LST is discussed. In the first step, multiple endmember spectral mixture analysis is applied to calculate land composition fractions. High resolution urban LST is then estimated through a linear combination of each thermal response component multiplied by their respective abundance in the second step. Evaluation of the proposed method was performed at four spatial scales (4m, 8m, 16m and 32m) over two scenes with different land cover characteristics. Results show a relative modest agreement was found between the estimated LST and the reference LST for both visual interpretation and quantitative accuracy metrics. Overall, this approach enhances the exploitation of hyperspectral data for estimating fine spatial resolution LST, therefore can serve as a promising technical in urban and environmental applications. © 2015 IEEE.

Original language	English
Title of host publication	2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015
Publisher	IEEE Computer Society
Volume	2015-June
ISBN (Print)	9781467390156
DOIs	https://doi.org/10.1109/WHISPERS.2015.8075498
Publication status	Published - 2 Jul 2015
Externally published	Yes
Event	7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015 - Tokyo, Japan Duration: 2 Jun 2015 → 5 Jun 2015

Publication series

Name	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume	2015-June
ISSN (Print)	2158-6276

Conference

Conference	7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015
Place	Japan
City	Tokyo
Period	2/06/15 → 5/06/15

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

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

SDG 11 Sustainable Cities and Communities

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Liu, K., Su, H., Wang, W., & Li, X. (2015). The estimation of high resolution urban surface temperature using hyperspectral spectral mixture analysis. In 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015 (Vol. 2015-June). Article 8075498 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2015-June). IEEE Computer Society. https://doi.org/10.1109/WHISPERS.2015.8075498

Liu, Kai ; Su, Hongbo ; Wang, Weimin et al. / The estimation of high resolution urban surface temperature using hyperspectral spectral mixture analysis. 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015. Vol. 2015-June IEEE Computer Society, 2015. (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing).

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title = "The estimation of high resolution urban surface temperature using hyperspectral spectral mixture analysis",

abstract = "In this paper, a simple method that uses hyperspectral spectral mixture analysis to estimate fine resolution urban LST is discussed. In the first step, multiple endmember spectral mixture analysis is applied to calculate land composition fractions. High resolution urban LST is then estimated through a linear combination of each thermal response component multiplied by their respective abundance in the second step. Evaluation of the proposed method was performed at four spatial scales (4m, 8m, 16m and 32m) over two scenes with different land cover characteristics. Results show a relative modest agreement was found between the estimated LST and the reference LST for both visual interpretation and quantitative accuracy metrics. Overall, this approach enhances the exploitation of hyperspectral data for estimating fine spatial resolution LST, therefore can serve as a promising technical in urban and environmental applications. {\textcopyright} 2015 IEEE.",

author = "Kai Liu and Hongbo Su and Weimin Wang and Xueke Li",

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Liu, K, Su, H, Wang, W & Li, X 2015, The estimation of high resolution urban surface temperature using hyperspectral spectral mixture analysis. in 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015. vol. 2015-June, 8075498, Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, vol. 2015-June, IEEE Computer Society, 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015, Tokyo, Japan, 2/06/15. https://doi.org/10.1109/WHISPERS.2015.8075498

The estimation of high resolution urban surface temperature using hyperspectral spectral mixture analysis. / Liu, Kai; Su, Hongbo; Wang, Weimin et al.
2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015. Vol. 2015-June IEEE Computer Society, 2015. 8075498 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2015-June).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - The estimation of high resolution urban surface temperature using hyperspectral spectral mixture analysis

AU - Liu, Kai

AU - Su, Hongbo

AU - Wang, Weimin

AU - Li, Xueke

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2015/7/2

Y1 - 2015/7/2

N2 - In this paper, a simple method that uses hyperspectral spectral mixture analysis to estimate fine resolution urban LST is discussed. In the first step, multiple endmember spectral mixture analysis is applied to calculate land composition fractions. High resolution urban LST is then estimated through a linear combination of each thermal response component multiplied by their respective abundance in the second step. Evaluation of the proposed method was performed at four spatial scales (4m, 8m, 16m and 32m) over two scenes with different land cover characteristics. Results show a relative modest agreement was found between the estimated LST and the reference LST for both visual interpretation and quantitative accuracy metrics. Overall, this approach enhances the exploitation of hyperspectral data for estimating fine spatial resolution LST, therefore can serve as a promising technical in urban and environmental applications. © 2015 IEEE.

AB - In this paper, a simple method that uses hyperspectral spectral mixture analysis to estimate fine resolution urban LST is discussed. In the first step, multiple endmember spectral mixture analysis is applied to calculate land composition fractions. High resolution urban LST is then estimated through a linear combination of each thermal response component multiplied by their respective abundance in the second step. Evaluation of the proposed method was performed at four spatial scales (4m, 8m, 16m and 32m) over two scenes with different land cover characteristics. Results show a relative modest agreement was found between the estimated LST and the reference LST for both visual interpretation and quantitative accuracy metrics. Overall, this approach enhances the exploitation of hyperspectral data for estimating fine spatial resolution LST, therefore can serve as a promising technical in urban and environmental applications. © 2015 IEEE.

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DO - 10.1109/WHISPERS.2015.8075498

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781467390156

VL - 2015-June

T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

BT - 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015

PB - IEEE Computer Society

T2 - 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015

Y2 - 2 June 2015 through 5 June 2015

ER -

Liu K, Su H, Wang W, Li X. The estimation of high resolution urban surface temperature using hyperspectral spectral mixture analysis. In 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015. Vol. 2015-June. IEEE Computer Society. 2015. 8075498. (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing). doi: 10.1109/WHISPERS.2015.8075498

The estimation of high resolution urban surface temperature using hyperspectral spectral mixture analysis

Abstract

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UN SDGs

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