Evaluation of super-resolution algorithms for mosaic hyperspectral imagery

Abstract

Hyperspectral imaging sensors acquire images in a large number of spectral bands, unlike traditional electro-optical and infrared sensors which sample only one or few bands. Hyperspectral mosaic sensors acquire an image of all spectral bands in one shot. Using a patterned array of spectral filters they measure different wavelength bands at different pixel locations, but this comes at the cost of a lower spatial resolution, as the sampling per spectral band is lower. Software algorithms can compensate for this loss in spatial sampling in each spectral channel. Here we compare the image quality obtained with spatial bicubic interpolation and two categories of super resolution algorithms: Two single frame super resolution algorithms which exploit spectral redundancies in the data and two multiframe super resolution algorithms which exploit spatio-Temporal structure. We make a quantitative assessment of the spatial and spectral image reconstruction quality on synthetic data as well as on semi-synthetic mosaic sensor data for applications in security and medical domains. Our results show that multi frame super resolution provides the best spatial and signal-To-noise quality. The single frame super resolution approaches score lower on spatial sharpness but do provide a substantial improvement compared to mere spatial interpolation, while providing in some cases the best spectral quality. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.