S$^{2}$MoINet: Spectral–Spatial Multiorder Interactions Network for Hyperspectral Image Classification

Deep learning methods have shown great promise in automatically extracting features from hyperspectral images (HSIs) for classification purposes. Recently, researchers have recognized the importance of high-order feature interactions—capturing relationships between features in different image regions—in extracting discriminative features. Despite their effectiveness, the existing deep learning models for HSI classification often overlook high-order feature interactions, resulting in suboptimal performance. To address this issue, we propose a novel spectral–spatial multiorder interaction network (S$^{2}$MoINet) for HSI classification. The proposed framework can effectively extract highly discriminative features by leveraging correlations between features in different locations, significantly improving the classification accuracy. More specifically, we design a multiorder spectral–spatial interaction block in the framework to extract the high-order and generalized features by leveraging the interaction between spatial and spectral features. Based on experimental results from four public HSI datasets, it has been shown that the proposed S$^{2}$MoINet delivers optimal classification results when compared to other state-of-the-art methods.