LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift
Modeling hyperspectral imagery (HSI) across different sensors presents a fundamental challenge due to variations in wavelength coverage, band sampling, and channel dimensionality. As a result, models trained under a fixed spectral configuration often fail to generalize to other sensors. Existing Vision Transformer (ViT) approaches either rely on implicit spectral modeling with fixed channel assumptions or adopt explicit spatial-spectral attention with prohibitive computational cost, leading to a fundamental trade-off between efficiency and expressiveness. In this work, we introduce Low-rank Efficient Spatial-Spectral ViT (LESSViT), a sensor-flexible architecture for cross-spectral general...
| Authors | Haozhe Si, Yuxuan Wan, Yuqing Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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