A novel meta-learning-based hyperspectral image classification algorithm

Aimed at the hyperspectral image (HSI) classification under the condition of limited samples, this paper designs a joint spectral–spatial classification network based on metric meta-learning. First, in order to fully extract HSI fine features, the squeeze and excitation (SE) attention mechanism is introduced into the spectrum dimensional channel to selectively extract useful HSI features to improve the sensitivity of the network to information features. Second, in the part of spatial feature extraction, the VGG16 model parameters trained in advance on the HSRS-SC dataset are used to realize the transfer and learning of spatial feature knowledge, and then, the higher-level abstract features are extracted to mine the intrinsic attributes of ground objects. Finally, the gated feature fusion strategy is introduced to connect the extracted spectral and spatial feature information on HSI for mining more abundant feature information. In this paper, a large number of experiments are carried out on the public hyperspectral dataset, including Pavia University and Salinas. The results show that the meta-learning method can achieve fast learning of new categories with only a small number of labeled samples and has good generalization ability for different HSI datasets.