Abstract With the rapid evolution of new engineered surfaces, there is a strong need for developing tools to measure and characterize these surfaces at different scales. In order to obtain all meaningful details of the surface at various required scales, data fusion can be performed on data obtained from a combination of instruments or technologies. In order to evaluate the fusion methods, typically, well-recognized images like ‘Lena’ are used. But surface metrology datasets are distinctly different from those images, as all the data points are in focus, compared to typical images with a subject in focus and background with various levels of out-of-focus. So, a performance study was conducted on a wide range of surface samples and it was shown that Regional Edge Intensity (REI) is the preferred fusion method for surface metrology datasets, and Regional Energy (RE) is the second preferred method, when single-scale performance metrics are considered.