Double-Attention Context Interactive Network for Hyperspectral Image Classification

Convolution is still the main method for hyperspectral image classification, since it takes into account both spatial and spectral characteristics. However, the convolution relies on local perceptual computation, ignoring the effective discriminant of context association for classification. In this paper, we propose a Double-Attention Context Interactive Network (DACINet) for hyperspectral image classification. Specifically, a Context Interaction Fusion Module (CIFM) is designed to enhance long-range contextual dependencies. By stacking multiple 3D convolutional layers, the module progressively enlarges its receptive field, while cross-layer residual connections facilitate the integration of features from different contextual scales, thereby strengthening the model’s ability to capture complex relationships within the hyperspectral data. Then, a Channel–Spatial Double-Attention (CSDA) mechanism based on 3D is proposed for enhancing the two-dimensional spatial features and one-dimensional spectral features, respectively, and fusing the enhanced features. Furthermore, we also construct a hybrid convolutional layer, which combines 2D and 3D convolution to further enhance spectral bands on the basis of three-dimensional understanding. Extensive experiments on the widely used IP, UP, SA and HU datasets show that the proposed DACINet achieves superior classification accuracy, reaching Overall Accuracies of 96.78%, 97.77%, 99.53% and 86.67% respectively, outperforming other state-of-the-art models.