Label Relationship Graph-Enhanced Class Hierarchy for Incremental Classification of Remote Sensing Images

Incremental learning is a strategy that continuously incorporates new data to tackle emerging tasks without the need for retraining the model. While effective, it encounters the challenge of catastrophic forgetting. Hierarchical Classification (HC) enhances classification accuracy and efficiency by assigning objects to multiple labels within a hierarchical structure. This paper introduces LRGIC, a novel approach specifically designed for incremental hierarchical classification of remote sensing images. LRGIC combines class hierarchy (CH), a Feature Pyramid Network (FPN), and a Learning Without Forgetting (LWF) strategy, while also incorporating a HEX graph to constrain labels and encode hierarchical knowledge. These elements, integrated within a hierarchical residual network, significantly boost classification performance. The FPN captures multi-scale features, and the LWF strategy facilitates the learning of new categories without reusing old samples. Experimental results demonstrate that LRGIC effectively classifies new categories and their hierarchical relationships, while preserving the performance of existing categories, underscoring its substantial research and practical value.