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Proceedings Paper

Use of Lidar data to geometrically constrain radiance spaces for physics-based target detection
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Paper Abstract

Upon collecting hyperspectral image (HSI) data, several steps are usually required prior to comparing sensor data to a target reflectance spectrum of interest. A common practice is the application of an atmospheric compensation routine, which converts a spectral cube from the radiance domain to the reflectance domain. Such routines may prove to be problematic when predicting reflectance spectra for subpixel targets under varying illumination conditions. An alternative to atmospheric compensation is to employ physics-based forward models to predict the ways that a target spectrum might appear at the sensor. Instead of generating a single target radiance vector, a target vector space is created, which theoretically spans all possible target manifestations in the sensor radiance cube. Typically, a background vector space is also generated using in-scene radiance vectors that are significantly unlike the target space. Target detection then occurs in the radiance domain by comparing a scene pixel's radiance spectrum to the target and background vector spaces. One disadvantage of using such physics-based model approaches is that the complexity of the radiance model drives the span of the target vector space. It may be possible to optimize the volume of target spaces on a local basis by incorporating spatial information, as provided by a geo-registered, co-temporal topographical Lidar data set. Such data may serve to eliminate geometric ambiguity at any given location in a scene; thus, the local target vector space may be constrained relative to the global target space. In doing so, target detection performance may be improved. A description of the various image processing techniques used to constrain target vector spaces is presented, including estimation of shadows, ground plane orientation, skydome visibility, and pixel purity. Finally, target detection performance resulting from the constrained vector space approach is discussed.

Paper Details

Date Published: 26 September 2007
PDF: 12 pages
Proc. SPIE 6661, Imaging Spectrometry XII, 66610J (26 September 2007); doi: 10.1117/12.729488
Show Author Affiliations
Michael S. Foster, Rochester Institute of Technology (United States)
Air Force Institute of Technology (United States)
John R. Schott, Rochester Institute of Technology (United States)
David W. Messinger, Rochester Institute of Technology (United States)
Rolando Raqueño, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 6661:
Imaging Spectrometry XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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