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Proceedings Paper

Characterization of physics-based radiative transfer modeling parameters for a blind test airborne hyperspectral data set
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Paper Abstract

This work was motivated by the availability of a new ground truthed hyperspectral data set, freely accessible to the scientific community for target detection algorithm testing. In our research, we are interested in physics-based approaches to target detection, i.e. those techniques aimed at modeling the radiation transfer within the atmosphere in order to account for atmospheric/viewing/illumination effects. This is a crucial aspect in target detection applications, where the available information resides in the sensor-acquired radiance image and field-measured spectral reflectances of the targets. Properly backing out the aforementioned effects allows detection to be performed in either of the two domains, i.e. radiance or reflectance. As part of our research into the use of physics-based radiative transfer modeling (RTM) for target detection with these new data, it was important to accurately analyze the available a priori information concerning data acquisition, and investigate the value of enhancing this information by making use of freely accessible meteorological and environmental data. In this work, the characterization procedure of the RTM parameters applied to these data is described, and the corresponding RTM parameters thus obtained are reported. A range of variation for some of these parameters were determined as well, in order to allow for a certain degree of variability around nominal conditions (e.g. spatial variability within the scene, non-perfect acquisition condition knowledge, etc.). Target detection results obtained by adopting the RTM parameters attained by the characterization procedure show similar performance in both the radiance and the reflectance domains.

Paper Details

Date Published: 13 May 2010
PDF: 12 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769526 (13 May 2010); doi: 10.1117/12.849829
Show Author Affiliations
Stefania Matteoli, Univ. of Pisa (Italy)
Emmett J. Ientilucci, Rochester Institute of Technology (United States)
John P. Kerekes, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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