A new conditional random field based on mixture of generalized Gaussian model for synthetic aperture radar image segmentation

摘要

In this paper, we propose a new algorithm using a conditional random field (CRF) model based on texture features for Synthetic Aperture Radar (SAR) image segmentation. Using the benefit of contourlet transform in describing the texture of SAR image, first, we extract contourlet coefficients from the image. Then, to take advantage of the simultaneous use of low- and high-frequency contourlet subbands, we apply the mixture of generalized Gaussian model (MoGGM) on all contourlet sub-bands to obtain more accurate texture features. Suggesting a new unary potential function based on MoGGM in our proposed CRF, we no longer require estimating the parameters in the multinominal logistic regression (MLR) model in previous CRF methods. Furthermore, we utilize the fifth-order moment to estimate the parameters of MoGGM for achieving better statistical properties of a specific region in SAR images and improving the accuracy of the texture recognition process. The experimental analysis demonstrates that segmentation results using the proposed algorithm are effectively improved compared to the previous CRF methods for SAR image segmentation.