A Novel Crop Classification Method Based on the Tensor-GCN for Time-Series PolSAR Data
Jianda Cheng, Deliang Xiang, Qiang Yin, Fan Zhang
Abstract
Time-series polarimetric synthetic aperture radar (PolSAR) has been proven to be an effective technique for crop classification and agricultural activity monitoring. However, the characterization and utilization of time-series PolSAR data by existing methods are still inadequate. They are unable to extract and utilize time-varying features, which can describe the dynamic changes of crop polarimetric information. In this paper, we propose a tensor form to comprehensively describe the information of time-series PolSAR data, including spatial context information, polarimetric scattering information, and temporal context information. And we define a novel similarity value for the tensors (TSV), which can simultaneously consider distance and shape similarity of tensors. Then, we construct a tensor-based graph representation to capture the global similarity information of time-series PolSAR data. Finally, we propose a tensor-based graph convolutional network (Tensor-GCN) to extract deep features of graph node tensors for crop classification. Experimental results and analysis on two time-series PolSAR data firmly demonstrate the superiority of the proposed Tensor-GCN to other state-of-the-art methods.