Resampling in hyperspectral cameras as an alternative to correcting keystone in hardware, with focus on benefits for optical design and data quality

Abstract

Current high-resolution hyperspectral cameras attempt to correct misregistration errors in hardware. Usually, it is required that aberrations in the optical system must be controlled with precision 0.1 pixel or smaller. This severely limits other specifications of the hyperspectral camera, such as spatial resolution and light gathering capacity, and often requires very tight tolerances. If resampling is used to correct keystone in software instead of in hardware, then these stringent requirements could be lifted. Preliminary designs show that a resampling camera should be able to resolve at least 3000-5000 pixels, while at the same time collecting up to four times more light than the majority of current high spatial resolution cameras that correct keystone in hardware (HW corrected cameras). A Virtual Camera software, specifically developed for this purpose, was used to compare the performance of resampling cameras and HW corrected cameras. For the cameras where a large keystone is corrected by resampling, different resampling methods are investigated. Different criteria are suggested for quantifying performance, and the tested cameras are compared according to these criteria. The simulations showed that the performance of a resampling camera is comparable to that of a HW corrected camera with 0.1 pixel residual keystone, and that the use of a more advanced resampling method than the commonly used linear interpolation – such as for instance high-resolution cubic splines – is highly beneficial for the data quality of the resampled image. Our findings suggest that if high-resolution sensors are available, it would be better to use resampling instead of trying to correct keystone in hardware. Keywords: