Heterogeneous Network-Based Contrastive Learning for Efficient PolSAR Land Cover Classification

The proposed Heterogeneous Network-Based Contrastive Learning (HCLNet) method aims to learn high-level representations from unlabeled PolSAR data by effectively utilizing multi-features and superpixels, addressing the challenges of limited labeled data and scattering confusion.