Interactive Three-dimension Group Convolution Network with Non-local Fusion Attention for Hyperspectral Image Classifcation

Three-dimensional convolutional networks (3DC-NNs) have proven to be powerful tools for hyperspectral image (HSI) classification. However, most existing 3DCNN networks optimize global spectral bands, neglecting the distinct reflective characteristics exhibited in different spectral ranges. To address this limitation, we propose a novel Interactive 3D Group Convolution Network (IGCNet) that incorporates a cross-group interaction strategy. This model explores unique information within different spectral ranges and leverages the high relevance between adjacent groups to enhance the complementarity of information. Additionally, to overcome the constraints of local receptive fields, a non-local fusion attention mechanisms is designed to account for the global correlations of feature map and emphasize important spatial regions during the fusion of group convolutional features, thereby improving the spatial-spectral representation capability of features. Experimental results on two data sets demonstrate the superior performance of IGCNet.