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Proceedings Paper

Spectral BRDF modeling of vehicle signature observations in the VNIR-SWIR
Author(s): T. Perkins; S. Adler-Golden; L. Muratov; R. Sundberg; E. Ientilucci; L. Cain
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Paper Abstract

Hyperspectral imaging (HSI) sensors have the ability to detect and identify objects within a scene based on the distinct attributes of their surface spectral signatures. Many targets of interest, such as vehicles, represent a complex arrangement of specular (non-Lambertian) materials with curved and flat surfaces oriented at varying view factors. This complexity, combined with possible changing atmospheric/illumination conditions and viewing geometries, can produce significant variations in the observed signatures from measurement to measurement, making detection and/or reacquisition challenging. This paper focuses on the characterization of visible-near infrared-short wave infrared (VNIR-SWIR) spectra for detection, identification and tracking of vehicles. Signature variations are predicted using a novel image simulation tool to calculate spectral images of complex 3D objects from a spectral material description such as the modified Beard-Maxwell BRDF model, a wireframe shape model, and a directional model of the illumination. We compare the simulations with recent VNIR-SWIR hyperspectral imagery of vehicles and panels collected at the Rochester Institute of Technology during an Autumn 2015 measurement campaign. Variations in both the simulated and measured spectra arise mainly from differences in the relative glint contribution. Implications of these variations on vehicle detection and identification are briefly discussed.

Paper Details

Date Published: 17 May 2016
PDF: 7 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400R (17 May 2016); doi: 10.1117/12.2225018
Show Author Affiliations
T. Perkins, Spectral Sciences, Inc. (United States)
S. Adler-Golden, Spectral Sciences, Inc. (United States)
L. Muratov, Spectral Sciences, Inc. (United States)
R. Sundberg, Spectral Sciences, Inc. (United States)
E. Ientilucci, Rochester Institute of Technology (United States)
L. Cain, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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