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Proceedings Paper

Spectral optimization studies and schemes to enhance target detection and display for a three-band staring LWIR sensor
Author(s): Rulon R. Mayer; James Waterman; Jonathon Schuler; Dean Scribner
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Paper Abstract

To achieve enhanced target discrimination, prototype three- band long wave infrared (LWIR) focal plane arrays (FPA) for missile defense applications have recently been constructed. The cutoff wavelengths, widths, and spectral overlap of the bands are critical parameters for the multicolor sensor design. Previous calculations for sensor design did not account for target and clutter spectral features in determining the optimal band characteristics. The considerable spectral overlap and correlation between the bands and attendant reduction in color contrast is another unexamined issue. To optimize and simulate the projected behavior of three-band sensors, this report examined a hyperspectral LWIR image cube. Our study starts with 30 bands of the LWIR spectra of three man-made targets and natural backgrounds that were binned to 3 bands using weighted band binning. This work achieves optimal binning by using a genetic algorithm approach and the target-to-clutter-ratio (TCR) as the optimization criterion. Another approach applies a genetic algorithm to maximize discrimination among the spectral reflectivities in the Non-conventional Exploitation Factors Data System (NEFDS) library. Each candidate band was weighted using a Fermi function to represent four interacting band edges for three- bands. It is found that choice of target can significantly influence the optimal choice of bands as expressed through the TCR and the Receiver Operator Characteristic curve. This study shows that whitening the image data prominently displays targets relative to backgrounds by increasing color contrast and also maintains color constancy. Three-color images are displayed by assigning red, green, blue colors directly to the whitened data set. Achieving constant colors of targets and backgrounds over time can greatly aid human viewers in the interpretation of the images and discriminate targets.

Paper Details

Date Published: 7 January 2004
PDF: 12 pages
Proc. SPIE 5159, Imaging Spectrometry IX, (7 January 2004); doi: 10.1117/12.506070
Show Author Affiliations
Rulon R. Mayer, Naval Research Lab. (United States)
SFA Inc. (United States)
James Waterman, Naval Research Lab. (United States)
Jonathon Schuler, Naval Research Lab. (United States)
Dean Scribner, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 5159:
Imaging Spectrometry IX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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