Region Partition based Hybrid Deep Network for Polarimetric SAR Image Classification

Junfei Shi, Linjing Xu, Haiyan Jin, Wei Wang, Rong Fei, Shanshan Ji

Abstract: The Convolutional Neural Network (CNN) model excels at learning local features, but struggles with capturing global large-scale features, particularly in extremely heterogeneous areas. In contrast, the Graph Convolution Network (GCN) is an effective tool for PolSAR image classification, demonstrating a capability to learn large-scale global features proficiently.To learn effective features for both heterogenous terrain objects and edge details well, a novel region partition based hybrid deep network is proposed for adaptive learning features for boundary and non-boundary regions, which can learn both large-scale global features for extremely heterogeneous terrain objects and pixel-wise features for edge details. The proposed method can effectively partition a PolSAR image into boundary and non-boundary regions, and design a CNN and GCN subnetworks for them respectively. Subsequently, a unified network is designed to effectively fuse both the advantages of GCN and CNN to enhance classification performance. The experiments verify the proposed algorithm can achieve better performance than compared methods in both region homogeneity and boundary preservation.