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Journal of Applied Remote Sensing

Synthetic advanced baseline imager true-color imagery
Author(s): Donald W. Hillger; Louie Grasso; Steven D. Miller; Renate Brummer; Robert J. DeMaria
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Paper Abstract

True-color imagery, which is formed via a weighted combination of red, green, and blue (RGB) spectral information, has important operational applications for qualitative environmental characterization, including the detection of smoke plumes, volcanic ash, and other aerosols that are not as easily discerned in conventional visible or infrared imagery, but may be more readily characterized via color properties. Despite its universal popularity, true-color is currently unavailable from geostationary satellites, and the next-generation GOES-R advanced baseline imager (ABI) will fall one band (green; 0.55 μm) short of doing so. However, approximations exist, and a process for simulating true-color imagery representative of capabilities anticipated from the ABI is presented and assessed here. High-resolution atmospheric model simulations are used to produce the ABI reflective band imagery required for true-color imagery. Those simulations are then rendered at ABI spatial (0.5-km visible) and temporal (5 min) resolution, to provide realistic data, long before the anticipated 2015 launch of GOES-R. An additional analysis, a color-space transformation, is used to assess the true-color (RGB) ABI images. The resulting hue images verify the less-green bias in the synthetic-green band and synthetic-RGB images created on ABI simulated data. Assessing the deficiencies in the RGB process will hopefully lead to an improved and standard means for generating an RGB product from the ABI data stream. Finally, as one of the many product applications of true-color imagery, an example of synthetic true-color imagery with added smoke is presented. The incorporation of aerosol properties into simulated imagery may help reveal the limits of detectability for atmospheric aerosols with future ABI.

Paper Details

Date Published: 1 January 2011
PDF: 20 pages
J. Appl. Rem. Sens. 5(1) 053520 doi: 10.1117/1.3576112
Published in: Journal of Applied Remote Sensing Volume 5, Issue 1
Show Author Affiliations
Donald W. Hillger, Colorado State Univ. (United States)
Louie Grasso, Colorado State Univ. (United States)
Steven D. Miller, Colorado State Univ. (United States)
Renate Brummer, Colorado State Univ. (United States)
Robert J. DeMaria, Colorado State Univ. (United States)

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