Optical Engineering · 2015 · 29 citations · 10 references
Natural SciencesVision ScienceComputer VisionDigital ImagingRemote SensingDigital Image ProcessingGeometric CorrectionHyperspectral ImagingImage QualityImage Quality AssessmentImage Quality AssuranceChannelsHyperspectral Image SensorsImage Sharpness AssessmentMisregistration CorrectionPoint Spread FunctionPoint Spread Function AnalysisPoint Spread Function CharacterizationPoint Spread Function Estimation
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.
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Sidney F. Ray, W.C. Gates · The Journal of Photographic Science · 1994 · 173 citations
Natural Sciences, Physics, Optics +11
Karim Lenhard, Andreas Baumgartner, Thomas Schwarzmaier · IEEE Transactions on Geoscience and Remote Sensing · 2014 · 55 citations · Full text