Classification of very high resolution SAR images of urban areas using copulas and texture in a hierarchical Markov random field model

ASI SponsorVoisin, AAVoisinKrylov, VAVAKrylovMoser, GGMoser2020-09-172020-09-172013-01-01https://hdl.handle.net/20.500.13025/4011This letter addresses the problem of classifying synthetic aperture radar (SAR) images of urban areas by using a supervised Bayesian classification method via a contextual hierarchical approach. We develop a bivariate copula-based statistical model that combines amplitude SAR data and textural information, which is then plugged into a hierarchical Markov random field model. The contribution of this letter is thus the development of a novel hierarchical classification approach that uses a quad-tree model based on wavelet decomposition and an innovative statistical model. The performance of the developed approach is illustrated on a high-resolution satellite SAR image of urban areas.Hierarchical Markov random fields (MRFs) supervised classification synthetic aperture radar (SAR) textural features urban areas waveletsClassification of very high resolution SAR images of urban areas using copulas and texture in a hierarchical Markov random field modelArticle Journal10.1109/LGRS.2012.2193869http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=620336654dcce118580fe1368eeb5fd