Vutichart Buranasiri

Active learning with Fermat distances can dramatically enhance hyperspectral image classification accuracy while maintaining scalability for large datasets.

Unsupervised hyperspectral image clustering gets a boost by learning representations in latent space with masked autoencoders and vision transformers, leading to more faithful diffusion distances.