Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields Export

@article{citeulike:944855, abstract = {Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert-Peano scan of the image, constitute a fast and robust alternative to hidden Markov random field models for spatial regularization of image analysis problems, even though the latter provide a finer and more intuitive modeling of spatial relationships. We here compare the two approaches and show that they can be combined in a way that conserves their respective advantages. We also describe how the distribution families and parameters of classes with constant or textured radar reflectivity can be determined through generalized mixture estimation. Sample results obtained on real and simulated radar images are presented.}, author = {Fjortoft, R. and Delignon, Y. and Pieczynski, W. and Sigelle, M. and Tupin, F.}, citeulike-article-id = {944855}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1198658}, journal = {Geoscience and Remote Sensing, IEEE Transactions on}, keywords = {classification, fields, markov, random, sar, unsupervised}, number = {3}, pages = {675--686}, posted-at = {2006-11-15 15:56:16}, priority = {5}, title = {Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1198658}, volume = {41}, year = {2003} }

TY - JOUR ID - citeulike:944855 L3 - citeulike-article-id:944855 N2 - Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert-Peano scan of the image, constitute a fast and robust alternative to hidden Markov random field models for spatial regularization of image analysis problems, even though the latter provide a finer and more intuitive modeling of spatial relationships. We here compare the two approaches and show that they can be combined in a way that conserves their respective advantages. We also describe how the distribution families and parameters of classes with constant or textured radar reflectivity can be determined through generalized mixture estimation. Sample results obtained on real and simulated radar images are presented. IS - 3 JF - Geoscience and Remote Sensing, IEEE Transactions on EP - 686 TI - Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields VL - 41 SP - 675 KW - classification KW - fields KW - markov KW - random KW - sar KW - unsupervised AU - Fjortoft, R AU - Delignon, Y AU - Pieczynski, W AU - Sigelle, M AU - Tupin, F PY - 2003/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1198658 ER -