hyperSMARTIES: Physics-Aware Spectral Representation Learning for Remote Sensing

Unifying image representation learning is essential for enabling self-supervised pretraining in remote sensing to scale across diverse sensors. However, existing models are typically constrained to fixed, predefined band sets, or do not scale seamlessly to new generation hyperspectral sensors, where hundreds of spectrally narrow channels densely sample the electromagnetic spectrum. To address this, we introduce hyperSMARTIES, a unified foundation model for wavelength-continuous masked data modeling. hyperSMARTIES learns a unified spectral projection that embeds arbitrary bands using a single shared projection function, and employs a transformer block that adaptively aggregates per-band embeddings into a single spectral token conditioned by the central wavelengths of bands. This lifts the need for sensor/range-specific projection functions and enables unified projection across diverse spectral regimes. Furthermore, spectral consistency is enforced via flow matching over wavelengths: a wavelength-indexed velocity field is learned on band embeddings to enforce smooth, locally consistent evolution across neighboring wavelengths, hence reproducing the physical characteristics of hyperspectral signals. We pretrain hyperSMARTIES on hyperspectral imagery acquired by the EnMAP sensor, which provides fine-grained spectral sampling, and apply downstream transfer on multiple tasks with diverse sensors. hyperSMARTIES not only outperforms previous models with improved unseen-sensor generalization, but also shows wavelength-dependent consistency in the learned band embeddings.