Image sharpening by means of spectral unmixing: comparison among different techniques

Spatial details of surfaces acquired by means of imaging spectrometers and multiband cameras are degraded by many factors. The atmosphere placed between the instrument and the surface, optical aberrations and tracking errors are some sources. Due to these causes, the photons coming from the instantaneous field of view pertaining a certain pixel, are spread over a larger number of picture elements, causing a spatial filtering of the image. Natural surfaces are rarely composed of a single uniform material and, therefore, blurring causes also a mixing of spectra of mineralogic different units on the surface. The problem of image sharpening is then linked to that of spectral unmixing. In this work, we compare the use of different statistical techniques as, Principal Component Analysis, Linear Spectral Unmixing and Spectral Clustering for image sharpening purposes.