LAUSR

Nonlocal convolutional neural networks (CNNs) have difficulties in dealing with the imbalanced samples in hyperspectral images (HSIs) effectively, so the networks cannot achieve ideal experiment results. Therefore, this article proposes an adaptive projection attention-based simplified nonlocal neural network for HSI classification. First, the local information is calculated in horizontal and vertical directions. Then, the information is passed to a simplified nonlocal network to learn the global semantic information. The simplified nonlocal network can reduce information redundancy and improve classification accuracy at the same time. Second, the global semantic information is adaptively projected according to the spatial features and compressed using multiscale pooling layers. After that, the pooled results are reassigned channel weights through two fully connected layers and extended using multiscale pooling layers. Then, the extended features are concatenated with the global semantic information, which can alleviate the imbalanced sample existing in the dataset. Then, a simplified nonlocal approach is used to fuse shallow and deep information to improve the robustness and classification performance of the network. In this article, experiments of the proposed method are conducted on three widely used hyperspectral datasets compared with those of seven state-of-the-art algorithms, and satisfactory overall and average accuracies are achieved, demonstrating the effectiveness of the proposed algorithm.