Super resolution of multispectral images using locally adaptive models

In this paper we present a locally adaptive super resolution Bayesian methodology for pansharpening of multispectral images. The proposed method incorporates prior local knowledge on the expected characteristics of the multispectral images, uses the sensor characteristics to model the observation process of both panchromatic and multispectral images, and includes information on the unknown parameters in the model in the form of hyperprior distributions. Using real and synthetic data, the pansharpened multispectral images are compared with the images obtained by other parsharpening methods and their quality is assessed both qualitatively and quantitatively.