A contextual classification system for remote sensing using a multivariate Gaussian MRF model

We propose a spatial contextual classification system for remote sensing images. In the system the observed multispectral images are modeled with a multivariate Gaussian Markov Random Field (GMRF) model and the hidden classified image is modeled with another type of MRF model. The classification is carried out from the viewpoint of Maximum a Posteriori (MAP) estimation. One of the well-known problems of MAP estimation is its high computational complexity. One way to avoid this problem is a pixelwise classification that is successfully implemented on a computer with a clique-type block matrix notation of a multivariate GMRF local conditional density function (LCDF). The proposed system is applied to real remote sensing data