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Proceedings Paper

A new approach to infer surface emissivity parameters from longwave infrared hyperspectral measurements
Author(s): Kevin M. Lausten; Ronald G. Resmini
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Paper Abstract

A new approach to perform temperature/emissivity separation (TES) has been developed and is described in this paper. The Planck-Modeled Temperature Emissivity Separation (PM-TES) technique provides a unique approach to the derivation of surface emissivity parameters from longwave infrared (LWIR) hyperspectral imagery (HSI) without incorporating the direct assumptions required in most current TES techniques. Accurate calculation of emissivity from at-sensor radiance values is complicated by the impact of temperature on the detected signal. Known are the ground leaving radiance (GLR) values observed by the sensor (after atmospheric compensation), while unknown are the emissivity values and the temperature of the surface material. Emissivity is difficult to determine at this juncture because there are N+1 unknown terms (N being the number of spectral bands contained in the HSI dataset) and N known terms, resulting in an indeterminate problem. Prior methods have incorporated assumptions associated with one of the unknown terms, which allows for the calculation of all additional unknown terms (e.g., Kahle and Alley, 1992) [1]. While these techniques have shown success in a variety of circumstances, the accuracy of their results is most often dependant upon a singular assumption. Minor miscalculations in these assumptions are thus propagated through the resultant HSI dataset for the extent of spectral processing. The PM-TES technique proposed in this paper does not make specific assumptions about either the temperature or emissivity characteristics of the in-scene materials to calculate emissivity parameters which are comparable in accuracy to current standard industry techniques. The proposed technique requires the input of temperature and emissivity ranges over which the Planck function is modeled to solve for the relationship between GLR and emissivity terms.

Paper Details

Date Published: 8 May 2006
PDF: 10 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62331W (8 May 2006); doi: 10.1117/12.665233
Show Author Affiliations
Kevin M. Lausten, National Geospatial-Intelligence Agency (United States)
Ronald G. Resmini, National Geospatial-Intelligence Agency (United States)


Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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