A Robust Fuzzy C-Means Algorithm Based on Bayesian Nonlocal Spatial Information for SAR Image Segmentation

Abstract

The fuzzy c-means (FCM) algorithm and many improved algorithms incorporating spatial information have been proven to be effective in image segmentation. However, these methods are not adaptable to process synthetic aperture radar (SAR) images owing to the intrinsic speckle noise. Our solution, which enables the effective segmentation of SAR images by guaranteeing noise-immunity and edge detail preservation simultaneously, is to propose a robust FCM algorithm based on Bayesian nonlocal spatial information (RFCM $\_$ BNL). The nonlocal idea considers more useful information for generating an auxiliary image. We measure the similarity between patches by utilizing a dedicated noise model for SAR images, and then apply it to the Bayesian formulation. Then we derive a new statistical distance, which is insensitive to speckle noise. Additionally, we ensure that the algorithm is robust to outliers by employing the entropy of the local gray-level histogram to control the extent to which the nonlocal spatial information term is adaptive to pixels. Experiments on simulated and real SAR images show that RFCM $\_$ BNL obtains the best result for SAR image segmentation compared with seven other fuzzy clustering algorithms.