Incremental Classification of Cross-Scene Hyperspectral Images Based on Dual Constraints and Knowledge Transfer

With the rapid advancement of hyperspectral imaging technology, there has been a dramatic surge in the volume of hyperspectral data, presenting unprecedented challenges for the incremental classification of hyperspectral images (HSI). In cross-scene HSI incremental classification, two critical challenges emerge: (1) the effectiveness of traditional sample replay methods is limited by high-dimensional data storage and privacy protection constraints; and (2) diverse scenes lead to catastrophic forgetting during new task learning. To address these challenges, this paper proposes an Incremental Classification algorithm based on Dual Constraints and Knowledge Transfer (IC-DCKT) for cross-scene hyperspectral images. First, to overcome the limitations of traditional replay methods in data storage and privacy protection, IC-DCKT innovatively introduces a sample-free storage approach. By combining regularization with knowledge distillation techniques, it achieves efficient knowledge transfer and retention. Second, to effectively mitigate catastrophic forgetting, the algorithm implements a dual-constraint mechanism: the approximate Null Space Projection Constraint (NSPC) restricts gradient update directions to preserve historical task feature distributions, while the Cosine Similarity Distillation Constraint (CSDC) enforces feature alignment between old and new models, significantly enhancing the model’s ability to retain knowledge of old tasks. Finally, this paper pioneers the integration of the pretrained hyperspectral large model HyperSIGMA into an incremental learning framework. The Feature Alignment Loss (FAL) not only improves the speed of new task learning but also compensates for the loss of critical information in new tasks caused by gradient update direction constraints imposed by NSPC. Experimental results on three hyperspectral datasets demonstrate that the proposed IC-DCKT method outperforms existing state-of-the-art incremental learning approaches.