Superpixel-based hybrid discriminative random field for fast PolSAR image classification

Abstract

Performance of the powerful discriminative random field (DRF) model for image processing and analysis is easily affected by the inherent speckle noise and the time-consuming iteration. Therefore, in this paper, a superpixel-based hybrid DRF (sp-HDRF) model is proposed for fast polarimetric synthetic aperture radar (PolSAR) image classification. The sp-HDRF model realizes the classification by two steps. First, the simple linear iterative clustering algorithm, which is modified by introducing the ratio of exponentially weighted averages operator, is utilized to obtain a superpixel graph with more accurate edge locations. Second, the conditional posterior distribution and the inference formula of the sp-HDRF model are derived on the superpixel graph, which is a generalization of a DRF model on the pixel. Then, the sp-HDRF model is applied to implement classification. Finally, the sp-HDRF model has the fusion of the polarimetric scattering features, the statistics, and the spatial relationships of the image. The experimental results on the real PolSAR images demonstrate the effectiveness of the sp-HDRF model and illustrate that it can provide stronger noise immunity, obtain smoother homogeneous areas in classification, and enhance the computational efficiency simultaneously.