Affine compensation of illumination in hyperspectral remote sensing images

A problem when working with optical satellite or airborne images is the need to compensate for changes in the illumination conditions at the time of acquisition. This is particularly critical when working with time series of data. Atmospheric correction strategies based on radiative transfer codes may provide a rigorous solution but it may not be the best solution for situations where a huge amount of hyperspectral images may need to be processed and computational time is a critical factor. The GMES (¿Global Monitoring for Environment and Security¿) initiative has promoted the creation of a new generation of satellites (the SENTINEL series) with ¿ultra-high resolution¿ and ¿superspectral imaging¿ capabilities. Therefore, there is an urgent need to quickly and reliably compensate for changes in the atmospheric transmittance and varying solar illumination conditions. In this paper three different forms of affine transformation models (general, particular and diagonal) are considered as candidates for rapid compensation of illumination variations. They are tested on a series of simulated multispectral images of Top-Of-Atmosphere (TOA) radiance, where the surface is a synthetic scene of a test site in Spain called Barrax, where reference data for validation is available. The results indicate that in 2 of the more moderate Sun positions, for all the Visibilities tested, the particular affine method is better than the other 2. The results also indicate that the proposed methodology is satisfactory for practical normalization of varying illumination and atmospheric conditions in remotely sensed images required for operational or time critical applications.