Accurate human perceived color cannot be obtained from most multispectral imagery (MSI) sensors without postprocessing, as sensor spectral response is often not in parity with the spectral sensitivities of the human visual system. In this experiment, 30 WorldView-2 and WorldView-3, 8-band visible-through-near-infrared (VNIR) images were collected over a color array consisting of 24 16-ft × 16-ft plywood targets painted to match the Macbeth ColorChecker scheme. The images were atmospherically compensated using the Empirical Line Method (ELM), and 6-band reflectance spectra covering the visible wavelengths were harvested from the image for each color target. These reflectance spectra were reconstructed to high-resolution spectra using spectral basis functions derived from a principal component analysis (PCA) performed on a set of high-resolution spectra measured from a calibrated color standard. Initial reconstruction performance was evaluated by comparing reconstructed spectra to high-resolution ground truth spectra using the Root Mean Square Error (RMSE). Reconstructed spectra were then converted to CIELAB (L*a*b*) values using the D65 illuminant, and CIE 1931 2° Standard Observer. Reconstructed L*a*b* values were compared to ground truth L*a*b* values using the CIEDE2000 (ΔΕ*00) color difference metric, and colorimetric accuracy of the process was evaluated. It was found that this method derived absolute colors with a median accuracy of ΔΕ*00 < 6 across all color targets and images.
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