LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift
Researchers have developed LESSViT, a novel architecture for hyperspectral imagery that addresses the challenge of generalizing models across different sensors. This Low-rank Efficient Spatial-Spectral ViT uses a structured low-rank factorization to efficiently model spatial-spectral interactions, significantly reducing computational complexity. The system also incorporates channel-agnostic patch embedding and wavelength-aware positional encoding to handle flexible spectral inputs, and is pre-trained using a hyperspectral masked autoencoder. AI
IMPACT Enhances the ability to use hyperspectral models across diverse sensor configurations, potentially broadening applications in remote sensing and material analysis.