Multi-temporal PolSAR crops classification using polarimetric-feature-driven deep convolutional neural network

Multi-temporal PolSAR data is suitable for crops classification and growth monitoring. It is still difficult to establish a classifier with good robustness and high generation over a long temporal acquisition duration. This work aims to provide a solution to this task by exploring benefits from both the target scattering mechanism interpretation and the advanced deep learning. A polarimetric-feature-driven deep convolutional neural network classification scheme is established. Comparison studies with multi-temporal UAVSAR datasets validate the efficiency and superiority of the proposal.