CrossACL: Analytic Continual Learning via Feature Cross for Hyperspectral Image Classification

Rapidly developing remote sensing technologies expand the volume and variety of hyperspectral images (HSIs). An HSI classification (HSIC) model should be able to adapt to new classes continually while retaining knowledge of previously learned classes to reduce training resources. However, popular HSIC models based on deep neural networks exhibit a significant performance decline in previously learned classes, known as the catastrophic forgetting phenomenon. To efficiently address this issue in HSIC, we propose an analytic continual learning method based on feature cross (CrossACL). CrossACL introduces a novel and training-free feature cross module (FCM) to better adapt to the increasingly complex feature space as the number of HSI classes increases. Furthermore, it utilizes an analytic recursive ridge regression classifier with a closed-form solution. This formulation achieves conditional equivalence between continual learning and joint training on all data seen so far, providing a theoretical guarantee against catastrophic forgetting. In addition, CrossACL introduces a simple but effective oversampling strategy to mitigate classification discrimination due to class-imbalanced HSI samples. Experiments on HSIC datasets demonstrate that CrossACL achieves competitive results compared with state-of-the-art methods at significantly lower computational consumption.