Data fusion for improving thermal emissivity separation from hyperspectral data

M. Shimoni; R. Haelterman; P. Lodewyckx

doi:10.1109/IGARSS.2015.7326435

Data fusion for improving thermal emissivity separation from hyperspectral data

M. Shimoni, R. Haelterman, P. Lodewyckx

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

Abstract

Land Surface Temperature (LST) and Land Surface Emissivity (LSE) are common retrievals from thermal hyperspectral imaging. However, their retrieval is not a straightforward procedure because the mathematical problem is ill-posed. This procedure becomes more challenging in an urban area where the spatial distribution of temperature varies substantially in space and time. In this study we propose a new method which integrates 3D surface information from LIDAR data in an attempt to improve the temperature and emissivity separation (TES) procedure for thermal hyperspectral scene. The experimental results prove the high accuracy of the proposed method in comparison to another conventional TES model.

Original language	English
Title of host publication	2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2955-2958
Number of pages	4
ISBN (Electronic)	9781479979295
DOIs	https://doi.org/10.1109/IGARSS.2015.7326435
Publication status	Published - 10 Nov 2015
Event	IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy Duration: 26 Jul 2015 → 31 Jul 2015

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2015-November

Conference

Conference	IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Country/Territory	Italy
City	Milan
Period	26/07/15 → 31/07/15

Keywords

Digital Elevation Model (DEM)
Land Surface Emissivity (LSE)
Land Surface Temperature (LST)
Temperature Emissivity Separation (TES)
Thermal hyperspectral

Access to Document

10.1109/IGARSS.2015.7326435

Cite this

Shimoni, M., Haelterman, R., & Lodewyckx, P. (2015). Data fusion for improving thermal emissivity separation from hyperspectral data. In 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings (pp. 2955-2958). Article 7326435 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2015.7326435

@inproceedings{bbd61c17d57444fca7d615a075b90614,

title = "Data fusion for improving thermal emissivity separation from hyperspectral data",

abstract = "Land Surface Temperature (LST) and Land Surface Emissivity (LSE) are common retrievals from thermal hyperspectral imaging. However, their retrieval is not a straightforward procedure because the mathematical problem is ill-posed. This procedure becomes more challenging in an urban area where the spatial distribution of temperature varies substantially in space and time. In this study we propose a new method which integrates 3D surface information from LIDAR data in an attempt to improve the temperature and emissivity separation (TES) procedure for thermal hyperspectral scene. The experimental results prove the high accuracy of the proposed method in comparison to another conventional TES model.",

keywords = "Digital Elevation Model (DEM), Land Surface Emissivity (LSE), Land Surface Temperature (LST), Temperature Emissivity Separation (TES), Thermal hyperspectral",

author = "M. Shimoni and R. Haelterman and P. Lodewyckx",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 ; Conference date: 26-07-2015 Through 31-07-2015",

year = "2015",

month = nov,

day = "10",

doi = "10.1109/IGARSS.2015.7326435",

language = "English",

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

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

pages = "2955--2958",

booktitle = "2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings",

}

Shimoni, M, Haelterman, R & Lodewyckx, P 2015, Data fusion for improving thermal emissivity separation from hyperspectral data. in 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings., 7326435, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2015-November, Institute of Electrical and Electronics Engineers Inc., pp. 2955-2958, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015, Milan, Italy, 26/07/15. https://doi.org/10.1109/IGARSS.2015.7326435

Data fusion for improving thermal emissivity separation from hyperspectral data. / Shimoni, M.; Haelterman, R.; Lodewyckx, P.
2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2955-2958 7326435 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2015-November).

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

TY - GEN

T1 - Data fusion for improving thermal emissivity separation from hyperspectral data

AU - Shimoni, M.

AU - Haelterman, R.

AU - Lodewyckx, P.

PY - 2015/11/10

Y1 - 2015/11/10

N2 - Land Surface Temperature (LST) and Land Surface Emissivity (LSE) are common retrievals from thermal hyperspectral imaging. However, their retrieval is not a straightforward procedure because the mathematical problem is ill-posed. This procedure becomes more challenging in an urban area where the spatial distribution of temperature varies substantially in space and time. In this study we propose a new method which integrates 3D surface information from LIDAR data in an attempt to improve the temperature and emissivity separation (TES) procedure for thermal hyperspectral scene. The experimental results prove the high accuracy of the proposed method in comparison to another conventional TES model.

AB - Land Surface Temperature (LST) and Land Surface Emissivity (LSE) are common retrievals from thermal hyperspectral imaging. However, their retrieval is not a straightforward procedure because the mathematical problem is ill-posed. This procedure becomes more challenging in an urban area where the spatial distribution of temperature varies substantially in space and time. In this study we propose a new method which integrates 3D surface information from LIDAR data in an attempt to improve the temperature and emissivity separation (TES) procedure for thermal hyperspectral scene. The experimental results prove the high accuracy of the proposed method in comparison to another conventional TES model.

KW - Digital Elevation Model (DEM)

KW - Land Surface Emissivity (LSE)

KW - Land Surface Temperature (LST)

KW - Temperature Emissivity Separation (TES)

KW - Thermal hyperspectral

UR - http://www.scopus.com/inward/record.url?scp=84962616861&partnerID=8YFLogxK

U2 - 10.1109/IGARSS.2015.7326435

DO - 10.1109/IGARSS.2015.7326435

M3 - Conference contribution

AN - SCOPUS:84962616861

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 2955

EP - 2958

BT - 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015

Y2 - 26 July 2015 through 31 July 2015

ER -

Shimoni M, Haelterman R, Lodewyckx P. Data fusion for improving thermal emissivity separation from hyperspectral data. In 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 2955-2958. 7326435. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2015.7326435

Data fusion for improving thermal emissivity separation from hyperspectral data

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this