Abstract
This paper investigates the improvement in sub-pixel target detection when image sharpening is applied to the data. A hyperspectral data cube was created using random linear mixtures of spectra and a grid of sub-pixel targets were inserted. The data cube was then convolved with a point-spread function to simulate blurring, noise was added and the output quantized. The resulting image cube is then pre-processed using various sharpening algorithms. We found that sharpening the hyperspectral cube generally increases the number of correctly identified sub-pixel targets compared to no pre-processing. In a simulation we quantified this result using a clutter matched filter ratio. We propose that all sub-pixel target detection algorithms could benefit from sharpening of the spectral cube.
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