A new pansharpening method using an explicit image formation model regularized via Total Variation

In this paper we present a new method for the pansharpening of multi-spectral satellite imagery. This method is based on a simple explicit image formation model which leads to an ill posed problem that needs to be regularized for best results. We use both Tikhonov (ridge regression) and Total Variation (TV) regularization. We develop the solutions to these two problems and then we address the problem of selecting the optimal regularization parameter λ. We find the value of λ that minimizes Stein´s unbiased risk estimate (SURE). For ridge regression this leads to an analytical expression for SURE while for the TV regularized solution we use Monte Carlo SURE where the estimate is obtained by stochastic means. Finally, we present experiment results where we use quality metrics to evaluate the spectral and spatial quality of the resulting pansharpened image.