Nonstationary PolSAR Image Classification by Deep-Features-Based High-Order Triple Discriminative Random Field

Abstract

Aiming at exploiting the discriminative deep features and encoding the high-level structures, this letter presents a deep-features-based high-order triple discriminative random field model, abbreviated as DF-HoTDF, for nonstationary polarimetric synthetic aperture radar (PolSAR) image classification. First, the DF-HoTDF model extracts the discriminative deep features by a graph-based complex-valued 3-D convolutional neural network (CV-3-D-CNN) and then constructs the unary potential by a negative log function. Second, it introduces an auxiliary field u to explicitly regulate the nonstationary label patterns of the PolSAR image and then constructs a pairwise potential guided by u to capture greater pairwise label interactions. Third, it defines a high-order potential on high-order cliques to encode high-level structures. Finally, under the discriminative model framework, the DF-HoTDF model has a weighted fusion of the unary potential, the pairwise potential, and the high-order potential. Then, with the DF-HoTDF model, we iteratively optimize the class label and the stationary maps until they converge. The experimental results demonstrate that the proposed DF-HoTDF model is of superior performances in nonstationary PolSAR image classification and that it can provide better label consistency in homogeneous region and better target structures and edge locations in heterogeneous region.