Enhancing Hyperspectral Image Classification in Small Sample Settings Using Conditional Denoising Diffusion Implicit Model

Enhancing classification accuracy in hyperspectral image classification under small sample scenarios remains a critical challenge. This study introduces a novel framework based on Conditional Denoising Diffusion Implicit Models (DDIM), featuring an adaptive synthetic sample generation strategy and label noise training. Our approach dynamically allocates synthetic samples for each class based on classification difficulty and generation reliability, while label noise training enhances the model’s generalization capabilities. As a flexible data augmentation method, our framework can be seamlessly integrated with various feature extraction techniques, demonstrating significant potential for further performance improvements. Validated on the Salinas and Pavia University datasets, our method highlights the effectiveness of diffusion models as a robust tool for addressing data scarcity, paving the way for broader applications in hyperspectral image classification and remote sensing.