Non-homogeneous AR model based prior for multiresolution fusion

In this paper we propose a model based approach for multiresolution fusion of remotely sensed images. Given a high spatial resolution & low spectral resolution PAN (Panchromatic) image and a low spatial resolution & high spectral resolution MS (Multispectral) images of the same geographical area, the objective is to enhance the spatial resolution of the MS images to that of the PAN image i.e. to obtain a high spatial and spectral resolution images. A proper regularization technique is required to address this ill posed problem and get a better solution. We first segment the PAN image using GMM (Gaussian Mixture Model) and extract AR (auto-regressive) parameters for each of the regions. We use a non-homogeneous AR model based prior for each of the fused MS images. The AR parameters, estimated from the PAN image are used in minimizing the cost function to obtain the high spatial resolution MS images. Experimental results are illustrated for Landsat-7 data set.