Estimating true color imagery for GOES-R

Michael D. Grossberg; Fazlul Shahriar; Irina Gladkova; Paul K. Alabi; Donald W. Hillger; Steven D. Miller

doi:10.1117/12.884020

20 May 2011 Estimating true color imagery for GOES-R

Michael D. Grossberg, Fazlul Shahriar, Irina Gladkova, Paul K. Alabi, Donald W. Hillger, Steven D. Miller

Author Affiliations +

Proceedings Volume 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII; 80481A (2011) https://doi.org/10.1117/12.884020
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

The Advanced Baseline Imager (ABI) on GOES-R will help NOAA's objective of engaging and educating the public on environmental issues by providing near real-time imagery of the earth-atmosphere system. True color satellite images are beneficial to the public, as well as to scientists, who use these images as an important "decision aid" and visualization tool. Unfortunately, ABI only has two visible bands (cyan and red) and does not directly produce the three bands (blue, green, and red) used to create true color imagery. We have developed an algorithm that will produce quantitative true color imagery from ABI. Our algorithm estimates the three tristimulus values of the international standard CIE 1931 XYZ colorspace for each pixel of the ABI image, and thus is compatible with a wide range of software packages and hardware devices. Our algorithm is based on a non-linear statistical regression framework that incorporate both classification and local multispectral regression using training data. We have used training data from the hyper-spectral imager Hyperion. Our algorithm to produce true color images from the ABI is not specific to ABI and may be applicable to other satellites which, like the ABI, do not have the ability to directly produce RGB imagery.

Citation Download Citation

Michael D. Grossberg, Fazlul Shahriar, Irina Gladkova, Paul K. Alabi, Donald W. Hillger, and Steven D. Miller "Estimating true color imagery for GOES-R", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80481A (20 May 2011); https://doi.org/10.1117/12.884020

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