A frequency and topology interaction network for hyperspectral image classification

Most existing Convolutional Neural Networks (CNNs), Transformers, and their variants have limitations in capturing relationships between hyperspectral image (HSI) data, leading to unclear descriptions of region boundaries and limited generalization abilities. While semi-supervised Graph Neural Networks (GNNs) come with higher computational costs. Therefore, this paper proposes a method interacting the frequency and topology information for HSI Classification to address the aforementioned shortcomings, which combines convolution and self-attention to capture both local and global contextual information, thereby enhancing feature representation. Additionally, this method focuses on exploring spectral and topological structure features and enhancing the information exchange and interaction to improve performance. Experimental results demonstrate that this method gains a competitive advantage in HSI classification by proving highly effective in handling spectral ambiguity and material heterogeneity. It also exhibits lower computational costs, making it more feasible and practical compared to most benchmark methods. Our code is available at https://github.com/youngboy03/FTINet.