Reproducibility of Spectral and Radiometric Normalized Similarity Indices for Multiband Images

Several tasks of remote sensing entail the measurement of the similarity/dissimilarity of a test multiband image to a reference multiband image. To this purpose, several indices has been developed over the last two decades. The most widely used indices are normalized to avoid dependence on the data format. In this work, we will focus on such indices and provide a novel insight on their behaviors. Spectral indices are those performing crossed measurements between couple of different bands of the test and reference image. Wherever crossed measurements do not occur, the index is purely spatial, or better radiometric. Both theoretical insights and simulations performed on a GeoEye dataset, with the products of twelve pansharpening methods, show that their performance ranking does not depend on the data format for purely radiometric indices, while it significantly depends on the data format, either spectral radiance or digital numbers (DN), for a purely spectral index, like the spectral angle mapper (SAM). The dependence on the data format is weak for indices that balance the spectral and radiometric similarity, like the family of indices, Q2n, based on hypercomplex algebra.