12 May 2010 Characterization of physics-based radiative transfer modeling parameters for a blind test airborne hyperspectral data set
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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.
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Stefania Matteoli, Stefania Matteoli, Emmett J. Ientilucci, Emmett J. Ientilucci, John P. Kerekes, John P. Kerekes, } "Characterization of physics-based radiative transfer modeling parameters for a blind test airborne hyperspectral data set", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769526 (12 May 2010); doi: 10.1117/12.849829; https://doi.org/10.1117/12.849829
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