In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model has been extended to the longwave infrared (LWIR). The model uses statistical descriptions of surface emissivities and temperature variations in a scene and propagates them through the effects of the atmosphere, the sensor, and processing transformations. A resultant system performance metric is then calculated based on these propagated statistics. This paper presents the theory and operation of extensions made to the model to cover the LWIR. Theory is presented on combining both surface spectral emissivity variation with surface temperature variation on the upwelling radiance measured by a downward-looking LWIR hyperspectral sensor. Comparisons of the model predictions with measurements from an airborne LWIR hyperspectral sensor at the DoE ARM site are presented. Also discussed is the implementation of a plume model and radiative transfer equations used to incorporate a thin man-made effluent plume in the upwelling radiance. Example parameter trades are included to show the utility of the model for sensor design and operation applications.

Date of creation, presentation, or exhibit



Proceedings of Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII 4381 (2001) 348-359 "Modeling of LWIR hyperspectral system performance for surface object and effluent detection applications," Proceedings of Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII. International Society of Optical Engineers. Held at AeroSpace in Orlando, Florida: 16-20 April 2001. Copyright 2001 Society of Photo-Optical Instrumentation Engineers. This paper was published in Proceedings of Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, SPIE vol. 4381 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. This work was sponsored by the Department of Defense under contract F19628-00-C-0002. ISSN: 0277-786X Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Conference Proceeding

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


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