Exemplar-Free Lifelong Hyperspectral Image Classification via Dual Constraints and Dynamic Margin Adaptation

Abstract

Hyperspectral image (HSI) lifelong classification aims to continuously recognize newly arriving classes while preserving previously acquired knowledge without full model retraining. Although exemplar replay is effective, retaining historical examples is often impractical in real-world applications due to privacy concerns and limited storage resources. Existing exemplar-free methods often suffer from representation drift in feature space and boundary confusion between previous and new classes. To address this issue, we propose an exemplar-free lifelong framework named DC-DMA for HSI classification based on dual-constrained feature extraction (DCFE) and dynamic margin adaptation (DMA). Specifically, DCFE is designed to stabilize spectral–spatial representation learning. It imposes an embedding consistency constraint to preserve low-level spectral–spatial patterns. DCFE also restricts deep parameter updates via the deep orthogonal gradient constraint to protect historical manifolds. Furthermore, to mitigate decision boundary ambiguity, we introduce a classifier with DMA, which maintains online class prototypes to estimate the semantic similarity between previous and new classes and accordingly adjusts class-specific cosine margins, thereby refining decision boundaries. Experiments on Indian Pines, Houston2013, Houston2018, and Hyrank-Loukia show that DC-DMA achieves competitive early-task performance and superior final-task performance against representative continual learning and hyperspectral incremental learning baselines.