Contextual interaction siamese network for few-shot hyperspectral image classification

In recent years, Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have achieved significant progress in Hyperspectral Image (HSI) classification. However, in practical applications, the high cost of sample annotation and the limited availability of training samples lead to overfitting in CNNs and ViTs under few-shot learning scenarios. Siamese networks, as an effective metric learning method, show promising performance in few-shot learning due to their low dependency on sample information. However, traditional siamese networks rely on static parameter-sharing mechanisms, lack feature interaction between the two subnetworks, and struggle to effectively capture the spatial-spectral heterogeneity in hyperspectral data. Additionally, they are prone to noise interference, resulting in insufficient discriminative power of key features. To address these challenges, this paper proposes a Contextual Interaction Siamese Network for Few-Shot Hyperspectral Image Classification (CISNet). First, an Interactive Feature Fusion Module (IFFM) is introduced to capture the similarities and differences between features from the two subnetworks, thereby enhancing the discriminative power of key features. Second, an Enhanced Token Generation Module (ETGM) is designed to generate correlated class tokens for the two subnetworks. Finally, this paper innovatively proposes a Context Interaction Transformer Block (CITB) and a Guided Attention (GA) mechanism to strengthen global context interaction between the two subnetworks. Extensive experiments demonstrate that CISNet achieves superior performance under few-shot conditions and outperforms other state-of-the-art methods in classification accuracy.