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A hierarchical Markovian model for multiscale region-based classification of vector-valued images
2005
IEEE Transactions on Geoscience and Remote Sensing
We propose a new classification method for vector-valued images, based on (i) a causal Markovian model, defined on the hierarchy of a multiscale region adjacency tree (MRAT), and (ii) a set of non-parametric dissimilarity measures that express the data likelihoods. The image classification is treated as a hierarchical labeling of the MRAT, using a finite set of interpretation labels (e.g. land cover classes). This is accomplished via a non-iterative estimation of the modes of posterior
doi:10.1109/tgrs.2004.842405
fatcat:yj2c3d6r2zc2ximvlurd5cycp4