To adapt Hyper-EI to handle diverse types of HSI data acquired by different sensors or in varying environments, transfer learning techniques can be employed. By pre-training the algorithm on a diverse set of hyperspectral datasets from different sensors and environments, Hyper-EI can learn generalized features and patterns that are applicable across various data sources.
Furthermore, domain adaptation methods can be utilized to fine-tune the pre-trained Hyper-EI model on specific datasets or sensor characteristics, enabling it to adapt and perform well on new, unseen data. By incorporating domain-specific knowledge during training, the algorithm can effectively handle variations in sensor characteristics, noise levels, and environmental conditions.
Moreover, data augmentation strategies tailored to specific sensor modalities or environmental conditions can be implemented to enhance the algorithm's robustness and generalization capabilities. By augmenting the training data with sensor-specific transformations or environmental variations, Hyper-EI can learn to inpaint missing data accurately across different HSI datasets and scenarios.