A Two-Component $K$–Lognormal Mixture Model and Its Parameter Estimation Method
Xin Zhou, Rongkun Peng, Congqing Wang · IEEE Transactions on Geoscience and Remote Sensing · 2014
Statistical models are used for describing the synthetic aperture radar (SAR) image data and are the basis of SAR image interpretations. Appropriate statistical models that can accurately describe the SAR image data are essential for the performances of SAR image interpretations. A statistical model, which is a mixture of K distribution and lognormal distribution, is proposed in this paper. This mixture model is able to model the clutter data, the target data, or the mixed data of clutter and target. This mixture model is also able to describe the proportions of clutter region and target region in a scene as well as the statistical properties of the clutter data and target data in the scene. A maximum likelihood method using the expectation-maximization approach is derived for estimating the parameters of the mixture model. Experiments have been conducted to demonstrate the effectiveness of the mixture model (together with the proposed parameter estimation method) for modeling the SAR data.