Heterogeneous Few-shot Learning with Knowledge Distillation for Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) classification is one of the most popular applications in remote sensing. In practice, due to the high cost of manual labeling, only a few hyperspectral image samples with labels can be obtained. A small number of labeled training samples tend to overfit the deep network method, resulting in a sharp decline in classification accuracy. In order to solve this problem, this paper proposes a classification method for hyperspectral images based on knowledge distillation and heterogeneous few-shot learning. Firstly, the model pretrain the feature extraction network on miniImageNet, a small sample natural image dataset with abundant labeled images, and introduces knowledge distillation to improve the feature expression capability of shallow network in small sample. Then, effective knowledge transfer is carried out between two heterogeneous data sets, and the weights obtained from the model on the natural data set are transferred to the backbone network of hyperspectral image classification to improve the accuracy of HSI classification. Finally, the classifier is fine-tuned on HSI using the paradigm of small sample learning to extract discriminative hyperspectral image features and further enhance the model's detail expression. Experimental results on two hyperspectral image classification datasets show that the proposed method can effectively improve the accuracy of small sample hyperspectral image classification.