Hyperspectral Image Classification Based on Multiscale Cross-Branch Response and Second-Order Channel Attention

Recently, most convolutional neural network-based methods use convolutional kernels of fixed size to extract features, which ignore the inherent spatial structure information of ground objects and lose spatial details. In addition, rough first-order statistics is not enough to capture subtle differences between different categories and extract nonlocal context information. To address these issues, a hyperspectral image (HSI) classification method based on multiscale cross-branch response and second-order channel attention (MCRSCA) is proposed in this article. First, a multiscale cross-branch response (MCBR) module is proposed, which uses convolution kernels of different sizes for feature extraction. It adds and concatenates the features of different scales, respectively, to obtain rich and complementary spatial context information. Then, element multiplication and element addition are performed on the fused multiscale features to promote the propagation of the multiscale information and enhance the nonlinear expression ability. Next, the second-order channel attention (SOCA) module is designed to interact the channel information through the feature covariance matrix to obtain the long-term dependence between channels. This module pays more attention to the significant channels and suppresses the redundant channels. Finally, the residual connection is used to embed MCBR and SOCA into the residual block to improve the gradient back propagation and accelerate the training process. Experiments on four commonly used HSI benchmark datasets show that the results of MCRSCA are competitive compared with other state-of-the-art methods.