Unsupervised Amplitude and Texture Classification of SAR Images With Multinomial Latent Model


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Kayabol K., Zerubia J.

IEEE TRANSACTIONS ON IMAGE PROCESSING, cilt.22, ss.561-572, 2013 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 22 Konu: 2
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1109/tip.2012.2219545
  • Dergi Adı: IEEE TRANSACTIONS ON IMAGE PROCESSING
  • Sayfa Sayıları: ss.561-572

Özet

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.