Boundary-adaptive MRF classification of optical very high resolution images

Urban area classification of very high resolution optical images relies on the one hand on the precise characterization of homogenous spectral responses within objects. On the other hand, sharp edges between the same objects, usual in man-made environments, have to be correctly detected. These two conflicting requirements make adaptive algorithms more suitable fo the task. The present work is devoted to introduce and validate one of these adaptive algorithms, based on Markov random fields (MRF) and neural networks, the approach works in a separate way on the two parts of the image, homogeneous and non.homogeneous ones, and allows to take into account their peculiarities. As such, it proves to be more reliable and accurate than basic maximum likelihood or even MRF and neural network classifiers considered alone.