IEEE Trans Image Process - Unsupervised amplitude and texture classification of SAR images with multinomial latent model.

Tópicos

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Resumo

In this paper, we combine amplitude and texture statistics of the synthetic aperture radar images for the purpose of model-based classification. In a finite mixture model, we bring together the Nakagami densities to model the class amplitudes and a 2-D auto-regressive texture model with t-distributed regression error to model the textures of the classes. A non-stationary multinomial logistic latent class label model is used as a mixture density to obtain spatially smooth class segments. The classification expectation-maximization algorithm is performed to estimate the class parameters and to classify the pixels. We resort to integrated classification likelihood criterion to determine the number of classes in the model. We present our results on the classification of the land covers obtained in both supervised and unsupervised cases processing TerraSAR-X, as well as COSMO-SkyMed data.

Resumo Limpo

paper combin amplitud textur statist synthet apertur radar imag purpos modelbas classif finit mixtur model bring togeth nakagami densiti model class amplitud d autoregress textur model tdistribut regress error model textur class nonstationari multinomi logist latent class label model use mixtur densiti obtain spatial smooth class segment classif expectationmaxim algorithm perform estim class paramet classifi pixel resort integr classif likelihood criterion determin number class model present result classif land cover obtain supervis unsupervis case process terrasarx well cosmoskym data

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