Prototypical Network With Residual Capsule for Few-Shot Hyperspectral Image Classification

While deep learning has been widely used in the hyperspectral image (HSI) classification, lacking labeled HSI poses significant challenges to effective and sufficient learning. To address this issue, this letter introduces a prototypical network with residual capsule (PN-ResCapsNet) for few-shot HSI classification. Compared with the convolutional neural networks (CNNs), the capsule networks can better capture spatial relationships. To better extract HSI features, residual structures and self-attention (SE) mechanisms are incorporated, which can overcome the limitation of shallow feature extraction in capsule networks. Moreover, a bias-reduction (BR) method, consisting of an interclass BR module and an intraclass BR module, is designed to rectify the prototypes, which can mitigate the bias between the support and the query set and alleviate the bias between the calculated prototype and the expected prototype. Experiments on widely used HSI datasets illustrate that the proposed method outperforms several state-of-the-art methods and achieves the overall accuracies of 92.07%, 81.02%, and 85.35% on Kennedy Space Center (KSC), Houston (HT), and Pavia University (PU) datasets, respectively.