Incremental Land Cover Classification via Soft Label and Sub-Region Distillation

With the exponential growth of satellite remote sensing data, land cover classification models must adapt continuously to new classes. However, conventional incremental learning methods face critical challenges: catastrophic forgetting degrades recognition of old classes, and the softmax function further suppresses old-class probabilities due to ”class crowding.” Existing distillation techniques also struggle to transfer features in irregular geospatial regions. To address these issues, we propose Soft Labels and Subregion Distillation (SLSRD). SLSRD mitigates class crowding by employing soft labels instead of hard labels, derived from a hybrid of softmax and sigmoid outputs that preserve richer probabilistic information. Concretely, the soft label is a convex combination of softmax- and sigmoid-based probabilities that preserves inter-class relations while relaxing over-confident exclusivity for newly introduced categories, and it supervises all pixels across stages. In parallel, a breadth-first search identifies subregions within each image, which are weighted by probability and size, and similarity between corresponding subregions of the old and new models is maximized. This dual strategy effectively transfers fine-grained knowledge and overcomes the limitations of conventional distillation methods, particularly for large-scale remote sensing imagery. Experiments on three benchmark datasets-Vaihingen, GID, and FBP-demonstrate that SLSRD outperforms traditional methods, significantly improving incremental land cover classification.