A lightweight dense relation network with attention for hyperspectral image few-shot classification

Deep learning methods have significantly progressed in hyperspectral image (HSI) classification. However, deep learning relies on large labeled data for training. The cost of labeling samples is enormous. In practical classification tasks, only a few labeled samples are available. A lightweight dense relation network with attention (LDA-RN) was presented for HSI few-shot classification. First, we design a 3D feature embedding module for spectral-spatial feature extraction. A light attention module (CSA), including the channel attention (CA) submodule and spatial attention (SA) submodule, is applied to reinforce essential features. Dense connections are used to fully utilize the feature information in each layer. Second, we propose a 2D relation learning module for classification by comparing the similarity between samples, using global average pooling to reduce the model parameters. Finally, meta-learning and fine-tuning training techniques are employed to enhance the model's generalization ability and reduce the dependence on the labeled samples. Experimental results on three available HSI datasets suggest that the proposed LDA-RN outperforms existing advanced few shot learning methods. For example, the experimental results show that the overall accuracy of the proposed method improves by 5.67% on the Univerisity of Pavia data set with only five labeled samples compared to the best method DCFSL.