Abstract
We propose a method for measuring and quantifying image quality in push-broom hyperspectral cameras in terms of spatial misregistration caused by keystone and variations in the point spread function (PSF) across spectral channels, and image sharpness. The method is suitable for both traditional push-broom hyperspectral cameras where keystone is corrected in hardware and cameras where keystone is corrected in postprocessing, such as resampling and mixel cameras. We show how the measured camera performance can be presented graphically in an intuitive and easy to understand way, comprising both image sharpness and spatial misregistration in the same figure. For the misregistration, we suggest that both the mean standard deviation and the maximum value for each pixel are shown. We also suggest how the method could be expanded to quantify spectral misregistration caused by the smile effect and corresponding PSF variations. Finally, we have measured the performance of two HySpex SWIR 384 cameras using the suggested method. The method appears well suited for assessing camera quality and for comparing the performance of different hyperspectral imagers and could become the future standard for how to measure and quantify the image quality of push-broom hyperspectral cameras.
Highlights
Hyperspectral cameras— called imaging spectrometers—are increasingly used for various military, scientific, and commercial purposes
We will explain what is meant by image sharpness, how the sharpness affects the errors caused by keystone and point spread function (PSF) variations, and how we suggest that this parameter be measured and quantified for a hyperspectral camera
The measurements are performed by moving a point source in subpixel steps along the pixel array in the across-track direction
Summary
Hyperspectral cameras— called imaging spectrometers—are increasingly used for various military, scientific, and commercial purposes. Spatial misregistration, caused by keystone and variations in the point spread function (PSF) across the spectral channels, distorts the captured spectra.[1] A similar error occurs in the spectral direction (spectral misregistration, caused by the smile effect and corresponding PSF variation) Quantifying these errors, as well as the image sharpness, would allow for evaluation and comparison of the performance of different hyperspectral imagers. The method we suggest in this paper fulfills these requirements and is based on a very basic principle: determine “how much of the energy collected by the hyperspectral camera ends up in the correct pixel in the final data cube.” When this is known, image sharpness and spatial and spectral misregistration can be determined. For the shorter and longer wavelengths, all the energy ends up in the pixel of interest, whereas for the middle wavelengths, part of the energy ends up in the neighboring pixels instead
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