Incremental Classification of Remote Sensing Images with Feature Pyramid and Class Hierarchy

Yang Chu, Yuntao Qian

Abstract: Incremental learning is a methodology aimed at addressing novel tasks by continuously acquiring new data while retaining knowledge obtained from previous learning tasks. Hierarchical classification (HC) assigns multiple labels to each object, establishing a hierarchical relationship among these labels. The objective of incremental HC for remote sensing images is to accurately differentiate recently added categories while preserving the HC structure. In this paper, we propose a novel incremental learning method called HC-FPNSP for HC, leveraging class hierarchy (CH), feature pyramid network (FPN), and learning without forgetting (LWF) strategy. This method introduces the information of CH into incremental learning for remote sensing image classification. Experimental results demonstrate the efficacy of the proposed method in accurately capturing the hierarchical relationships of newly added categories while maintaining satisfactory classification performance for existing categories.

External IDs:dblp:conf/igarss/ChuQ24