Composite Kernel and Hybrid Discriminative Random Field Model Based on Feature Fusion for PolSAR Image Classification

To effectively fuse the high-dimensional features, in this letter, we propose the composite kernel and hybrid discriminative random field model, abbreviated as CK-hybrid discriminative random field (HDRF), for polarimetric synthetic aperture radar (PolSAR) image classification. In the CK-HDRF model, given high-dimensional features with different characteristics, the unary potential is constructed by relating multiple kernel k-means (MKKM) clustering to the traditional HDRF model. In this way, the high-dimensional decomposition and texture features can be well fused, thus making their deserved contributions to the inference of the attributive class and further increasing the discrimination capacity of CK-HDRF. The pairwise potential is constructed by the generalized Ising model with an additional edge penalty function, and thus, it can well capture the underlying spatial relationship and maintain the edge locations in classification. Moreover, the statistics of PolSAR data are modeled by the Wishart-generalized gamma (WG Gamma) distribution. Experiments on real PolSAR images demonstrate the effectiveness of CK-HDRF in classification.

Automated summary

The letter introduces a composite kernel and hybrid discriminative random field model for PolSAR image classification, which effectively fuses high-dimensional features to enhance discrimination capacity. This model combines multiple kernel k-means clustering with traditional HDRF model to construct unary and pairwise potentials for feature fusion and spatial relationship capture, and utilizes the Wishart-generalized gamma distribution to model PolSAR data statistics. Experiments on real PolSAR images demonstrate the effectiveness of the model in classification.