Life-Long Learning With Continual Spectral-Spatial Feature Distillation for Hyperspectral Image Classification

The rapid development of hyperspectral remote sensing technology, has led to an explosion in the number of available hyperspectral images (HSI). The fast and accurate characterization of HSI poses a significant challenge for remote sensing scientists. Currently, deep learning strategies with various neural networks have been successfully applied for HSI classification using the concept of the "dataset-model". Still, there is a need to develop universal deep learning models for HSI classification using a continual updating strategy. This paper presents a life-long learning strategy to continually update model weights with the help of continual spectral-spatial feature distillation. Specifically, the proposed method introduces a spectral-spatial distillation strategy to retain knowledge of the previous well-trained model. Meanwhile, the learning metric term is integrated into a multi-level feature extraction to minimize the spectral-spatial feature discrepancy between the previous model and the new one. The experimental results indicate that our method achieves superior performance for continual HSI classification tasks without suffering from the persistent loss of characterization memory.