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Proceedings Paper

A framework for polarized radiance signature prediction for natural scenes
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Paper Abstract

As the interest in polarization sensitive imaging systems increases, the modeling tools used to perform instrument trade studies and to generate data for algorithm testing must be adapted to correctly predict polarization signatures. The incorporation of polarization into the image chain simulated by these tools must address the modeling of the natural illuminants (e.g. Sun, Moon, Sky), background sources (e.g. adjacent objects), the polarized Bidirectional Reflectance Distribution Function (pBRDF) of surfaces, atmospheric propagation (extinction, scattering and self-emission) and sensor effects (e.g. optics, filters). Although, each of these links in the image chain may utilize unique modeling approaches, they must be integrated under a framework that addresses important aspects such as a unified coordinate space and a common polarization state convention. This paper presents a modeling framework for the prediction of polarized signatures within a natural scene. The proposed image chain utilizes community developed modeling tools including an experimental version of MODTRAN and BRDF models that have been either derived or extended for polarization (e.g. Beard-Maxwell, Priest-Germer, etc.). This description also includes the theory utilized in the modeling tools incorporated into the image chain model to integrate these links into a full signature prediction capability. Analytical and experimental lab studies are presented to demonstrate the correct implementation and integration of the described image chain framework within the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model.

Paper Details

Date Published: 7 May 2007
PDF: 13 pages
Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65650Y (7 May 2007); doi: 10.1117/12.719798
Show Author Affiliations
Chabitha Devaraj, Rochester Institute of Technology (United States)
Scott Brown, Rochester Institute of Technology (United States)
David Messinger, Rochester Institute of Technology (United States)
Adam Goodenough, Rochester Institute of Technology (United States)
David Pogorzala, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6565:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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