A Bayesian Procedure for Full-Resolution Quality Assessment of Pansharpened Products

Pansharpening regards the fusion of a high-spatial resolution panchromatic image with a low-spatial resolution multispectral image. One of the most debated topics about pansharpening is related to the quality assessment of fused products. Two main assessment procedures are usually exploited in the literature: the reduced resolution validation and the full-resolution (FR) validation. The former has the advantage to be accurate, but the hypothesis of invariance among scales has to be assumed. The latter overcomes this limitation but paying it with a lower accuracy. In this paper, we will focus on the FR assessment proposing an approach for estimating an overall quality index at FR by using multiscale FR measurements. The problem is recast into the sequential Bayesian framework exploiting a Kalman filter to find its solution. The proposed procedure for quality evaluation has been tested on four real data sets acquired by the Pleiades, the GeoEye-1, the WorldView-3, and the WorldView-4 sensors assessing the quality of 19 pansharpened methods. The proposed approach has demonstrated its superiority with respect to the benchmark consisting of state-of-the-art quality assessment procedures.