Fine-grained crop classification from satellite image time series by boosting rare-class representations and enforcing global semantic consistency

Accurate and timely fine-grained crop type classification from satellite image time series is crucial for large-scale agricultural monitoring and decision support in food-security management. However, fine-grained classification remains challenging due to extreme class imbalance and high inter-crop spectral similarity, especially when rare crops occupy only small and fragmented parcels. We propose a rare-class-aware framework with global semantic consistency regularization for fine-grained crop