We apply a general form of affine transformation model to compensate illumination variations in a series of multispectral images of a static scene and compare it to a particular affine and a diagonal transformation models. These models operate in the original multispectral space or in a lower-dimensional space obtained by Singular Value Decomposition (SVD) of the set of images. We use asystem consisting of a multispectral camera and a light dome that allows the measurement of multispectral data under carefully controlled illumination conditionsto generate a series of multispectral images of a static scene under varying illumination conditions. We evaluate the compensation performance using the CIELABcolour difference between images. The experiments show that the first 2 models perform satisfactorily in the original and lower dimensional spaces.