Lifelong Learning With Adaptive Knowledge Fusion and Class Margin Dynamic Adjustment for Hyperspectral Image Classification

Abstract

With the rapid growth in satellite imagery acquisition and decreasing revisit intervals, efficient on-orbit processing of hyperspectral data has become critical due to limited onboard computing resources. In this context, lifelong learning (LLL) offers a promising solution to enable continuous learning from new data without storing all previous data or retraining from scratch. However, the plasticity-stability dilemma remains a significant challenge, particularly in hyperspectral image (HSI) classification under class-incremental scenarios. To address this, we propose a novel network architecture that integrates contrastive learning and an angular penalty loss. The contrastive learning module facilitates adaptive knowledge fusion, enabling the model to effectively incorporate new information while preserving prior knowledge. The angular penalty loss allows the classifier to dynamically expand for new classes while maintaining discrimination between old and new categories. Together, these components ensure robust knowledge retention, transfer, and adaptability. Experimental results on three benchmark hyperspectral datasets demonstrate that our method significantly outperforms existing approaches, highlighting its efficacy in addressing LLL challenges in HSI classification. The code is available at https://github.com/Li-ZK/LLL-AFCA.