The new geostationary satellites, G-16 and Himawari-8 carry high-resolution advanced baseline imagers, ABI and AHI. The ABI onboard G-16 provides imagery in two narrow visible bands (red, blue), while ABI’s twin sensor AHI onboard Himawari-8 also has a green band, which allows the direct production of true color (RGB) images for AHI. Since natural color images are easier for both meteorologists and the public to interpret, it is important to provide true color imagery from geostationary orbit.
In this paper we present a method to estimate green band for ABI from available visible and near-IR bands by building a statistical predictor trained on AHI data. Simple approaches such as look-up-table or simple linear regression on the multi-spectral input parameters may produce satisfactory results globally, but will fail to correctly estimate green band in some cases due to the underlying non-linearity of the data.
We will present an approach which uses piecewise multi-linear regression on the multi-spectral input to train the green channel predictor. Our predictor is built from the combination of a classifier followed by a multi-linear function. Based on the values from the ABI bands, the classifier assigns each pixel to a class. Each class as an associated set of coefficients determining a multi-linear predictor mapping the ABI multi-spectral values to a predicted green value. This combination of a categorical classifier with per-class multi-linear function combines the efficiency of linear map while still preserving flexibility and accuracy by adjusting the number of classes.
Irina Gladkova, Michael Grossberg, and Don Hillger, "Virtual true-color imagery for advanced baseline imager (Conference Presentation)," Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 1064417 (Presented at SPIE Defense + Security: April 19, 2018; Published: 14 May 2018); https://doi.org/10.1117/12.2304509.5783303063001.
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