Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model

1. 1Utilize a Mixup foundation model to leverage few-shot learning capabilities
2. 2Apply cross-domain techniques to adapt the model to new, unseen domains
3. 3Fine-tune the model with a limited number of samples from the target domain
4. 4Evaluate the model's performance on the target domain using metrics such as accuracy and F1-score

ML researchers and engineers working on computer vision and hyperspectral image classification tasks can benefit from this approach to improve model performance and adaptability across different domains

💡 The proposed approach can effectively adapt to new domains with limited samples, reducing the need for extensive data augmentation and model updates

🚀 Cross-domain few-shot learning for hyperspectral image classification using Mixup foundation model! 📸