Few-Shot hyperspectral image classification with mamba and manifold convolution fusion network

Efficient modeling of global-local features is crucial for hyperspectral image (HSI) classification. The mamba network demonstrates strong capability in capturing global dependencies in HSI classification tasks, primarily utilizing a state-space model to extract first-order statistical features of spectral-spatial information in euclidean space, providing an initial representation of data characteristics. However, under few-shot conditions, fully exploiting effective features from limited samples and overcoming challenges such as class overlap and feature space sparsity caused by the insufficient extraction of second-order statistical features in riemannian space remain major research challenges. Therefore, we propose a dual branch manifold convolution-mamba network (DBMCMamba) for HSI classification. Specifically, it adaptively fuses forward and backward information through the vision mamba (Vim) block and utilizes the S6 module to extract global information, thereby enhancing global feature extraction capability. Meanwhile, the manifold convolution module extracts first-order statistical features of spectral-spatial information through convolutional layers and learns second-order statistics via the SPD manifold to strengthen DBMCMamba’s local feature representation under few-shot conditions. Finally, global and local features are fused for classification, effectively improving the accuracy and performance of HSI classification. On the Indian Pines, Pavia University, HongHu, and HanChuan datasets, DBMCMamba achieved classification accuracies of 95.23 %, 95.80 %, 95.58 %, and 94.93 %, respectively. Experimental results show that DBMCMamba demonstrates significant performance improvements compared to the state-of-the-art classification models. The code will be available online at https://github.com/ASDFFGG121EAA/DBMCMamba.