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Proceedings Paper

Impact of BRDF on physics-based modeling as applied to target detection in hyperspectral imagery
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Paper Abstract

Traditional approaches to hyperspectral target detection involve the application of detection algorithms to atmospherically compensated imagery. Rather than compensate the imagery, a more recent approach uses physical models to generate radiance signature spaces. The signature space is actually a representation of what the target might look like to the sensor as the reflectance propagates through the atmosphere. The model takes into account atmospherics, illumination conditions and target reflectivity. It is well known that the directional characteristics of reflectance vary considerably fromspecular to ideally diffuse (i.e., Lambertian). The current physical models assume the world is Lambertian. However, the reflectance properties are a function of wavelength, illumination angle, and viewing angle. The bidirectional reflectance distribution function (BRDF), which is actually a scattering function analogous to the angular scattering coefficient, describes the bidirectional reflectance values of all combinations of input-output angles and wavelength. This paper examines the impact of using the Lambertian assumption as it relates to physics bases material detection. The bidirectional reflectance studied is parameterized, based on laboratory measurements, using the Beard- Maxwell model. This parameterized reflectance is then coupled to the physics-based sensor-reaching radiance model to generate signature spaces. The signature spaces, along with hyperspectral imagery, are used in a target detection scheme where results are assessed through visual analysis.

Paper Details

Date Published: 27 April 2009
PDF: 12 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340T (27 April 2009); doi: 10.1117/12.819332
Show Author Affiliations
Emmett J. Ientilucci, Rochester Institute of Technology (United States)
Michael Gartley, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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