A Hierarchical Self-Consistent Regularization Approach to Satellite Image Time Series Classification

ArXi:2510.04916v2 Announce Type: replace Deep learning has become increasingly important in remote sensing image classification due to its ability to extract semantic information from complex data. Classification tasks often include predefined label hierarchies that represent the semantic relationships among classes. However, these hierarchies are frequently overlooked, and most approaches focus only on fine-grained classification schemes.