TerraMAE: learning spatial-spectral representations from hyperspectral Earth observation data via adaptive masked autoencoders

Abstract

Masked Autoencoders struggle with hyperspectral satellite imagery containing 200+ spectral bands, as uniform masking across all channels obscures critical spatial-spectral relationships. We introduce TerraMAE, which employs an adaptive channel grouping strategy to organize bands into statistically coherent groups with independent masking. Together with a customized loss function, this data-driven grouping strategy enables TerraMAE to learn robust spatial-spectral representations from unlabeled HSI. Experiments demonstrate that TerraMAE significantly outperforms baseline Masked Autoencoder and supervised ResNet-50 on soil texture prediction, achieving 15.7% and 6.6% lower error, respectively.