Expansion Spectral-Spatial Attention Network for Hyperspectral Image Classification.

Deep learning is increasingly used for the classification of hyperspectral images (HSI), thanks to its ability to completely utilize the rich characteristics of this type of imagery. However, at present, most classification models proposed for processing HSI data are based on standard convolution neural networks, which prefer to learn local information rather than global information, so that it is difficult to achieve ideal accuracy in the case of insufficient training samples in real applications. In this article, we propose a novel expansion spectral-spatial attention network (ESSAN) for HSI data classification in cases of insufficient training samples. First, a dual-branch network based on expansion convolution is employed as the model backbone to extract spectral and spatial information. All feature maps produced during the dual-branch process are superimposed to combine deep and shallow features by the ResNet concept. With the design philosophy of the superposition of expansion convolutional layers, the network can increase the receptive field to gather more global contextual information. Second, the model also includes a coordinate attention block, which directs the network to weight features according to their significance and suppresses those that are irrelevant. Finally, the method was tested on the four datasets from Matiwan Village, Pavia Center, Pavia University, and Shenzhen University, utilizing 1%, 1%, 5%, and 0.2% training samples, respectively. The results showed the overall accuracies, in order, 97.96%, 99.12%, 98.73%, and 99.36%. The preliminary results demonstrate the higher efficacy and accuracy of the proposed ESSAN in HSI data classification than the other state-of-the-art.