LAUSR

Deep learning has achieved impressive results on hyperspectral image (HSI) classification, which generally requires sufficient training samples and a huge number of parameters. However, it is challenging to label HSIs, and likely only a few samples are available in practice. Learning a large number of parameters by the model is also resource-intensive. This letter proposes an HSI classification model that achieves promising classification performance with fewer parameters in few-shot settings. The proposed model adopts the residual 3-D-convolution neural network (CNN) as a feature extraction network, and contrastive learning is introduced to learn more discriminative representations for HSIs which can conquer the obstacles from HSIs’ high interclass similarity and large intraclass variance. The proposed few-shot contrastive learning HSI classification model is tested on five popular HSI datasets and outperforms the state-of-the-art models.