Incremental classification of remote sensing images using feature pyramid and class hierarchy enhanced by label relationship graphs

Yang Chu, Yuntao Qian

Abstract: Incremental learning is a machine learning strategy that enables the integration of new data to address emerging tasks without retraining the model from scratch. This approach retains previously acquired knowledge and conserves resources, yet it faces the challenge of catastrophic forgetting. Hierarchical classification (HC) improves accuracy and efficiency by assigning labels with hierarchical relationships to objects. In this work, we propose CHFL, an incremental learning method for remote sensing images. CHFL leverages hierarchical relationships through a label relationship graph and class hierarchy information to encode knowledge effectively. It integrates a Feature Pyramid Network (FPN) to process multi-scale features, capturing discriminative region information, and a Learning Without Forgetting (LWF) strategy to efficiently learn new classes while preserving performance on previous ones. Additionally, a Hierarchy and Exclusion (HEX) graph is introduced to constrain label predictions, enhancing consistency and improving classification accuracy. Experimental results on the high-resolution remote sensing dataset HRSC show that CHFL achieves an accuracy of 93.82% on new class classifications while maintaining competitive performance on previous classes. Compared with existing methods, CHFL demonstrates superior classification performance, effectively mitigating catastrophic forgetting and addressing scale variations in remote sensing imagery.

External IDs:dblp:journals/apin/ChuQ25