Heterogeneous Few-Shot Learning for Hyperspectral Image Classification

Deep learning has achieved great success in hyperspectral image (HSI) classification. However, its success relies on the availability of sufficient training samples. Unfortunately, the collection of training samples is expensive, time-consuming, and even impossible in some cases. Natural image datasets that are different from HSI, such as Image Net and mini-ImageNet, have abundant texture and structure information. Effective knowledge transfer between two heterogeneous datasets can significantly improve the accuracy of HSI classification. In this letter, heterogeneous few-shot learning (HFSL) for HSI classification is proposed with only a few labeled samples per class. First, few-shot learning is performed on the mini-ImageNet datasets to learn the transferable knowledge. Then, to make full use of the spatial and spectral information, a spectral-spatial fusion network is devised. Spectral information is obtained by the residual network with pure 1-D operators. Spatial information is extracted by a convolution network with pure 2-D operators, and the weights of the spatial network are initialized by the weights of the model trained on the mini-ImageNet datasets. Finally, few-shot learning is fine-tuned on HSI to extract discriminative spectral-spatial features and individual knowledge, which can improve the classification performance of the new classification task. Experiments conducted on two public HSI datasets demonstrate that the HFSL outperforms the existing few-shot learning methods and supervised learning methods for HSI classification with only a few labeled samples. Our source code is available at https://github.com/Li-ZK/HFSL.

Automated summary

This study proposes a heterogeneous few-shot learning (HFSL) method for hyperspectral image (HSI) classification, which utilizes few-shot learning and knowledge transfer on different datasets to make full use of spectral and spatial information, improving classification accuracy.