A hierarchical Markovian model for multiscale region-based classification of vector-valued images

A. Katartzis, I. Vanhamel, H. Sahli
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
more » ... (MPM), inspired from existing approaches for Bayesian inference on the quadtree. The paper describes the main principles of our method and illustrates classification results on a set of artificial and remote sensing images, together with qualitative and quantitative comparisons with a variety of pixel-based techniques that follow the Bayesian-Markovian framework either on hierarchical structures or the original image lattice.
doi:10.1109/tgrs.2004.842405 fatcat:yj2c3d6r2zc2ximvlurd5cycp4