Benchmarking foundation models for hyperspectral image classification: Application to cereal crop type mapping
It addresses the underexplored potential of foundation models for operational hyperspectral crop mapping, providing a systematic evaluation for researchers and practitioners in Earth observation.
This study benchmarked three foundation models for cereal crop mapping using hyperspectral imagery, finding that the SpectralEarth model achieved the highest overall accuracy of 93.5% (+/- 0.8%), while HyperSigma and DOFA performed lower at 34.5% and 62.6%, respectively.
Foundation models are transforming Earth observation, but their potential for hyperspectral crop mapping remains underexplored. This study benchmarks three foundation models for cereal crop mapping using hyperspectral imagery: HyperSigma, DOFA, and Vision Transformers pre-trained on the SpectralEarth dataset (a large multitemporal hyperspectral archive). Models were fine-tuned on manually labeled data from a training region and evaluated on an independent test region. Performance was measured with overall accuracy (OA), average accuracy (AA), and F1-score. HyperSigma achieved an OA of 34.5% (+/- 1.8%), DOFA reached 62.6% (+/- 3.5%), and the SpectralEarth model achieved an OA of 93.5% (+/- 0.8%). A compact SpectralEarth variant trained from scratch achieved 91%, highlighting the importance of model architecture for strong generalization across geographic regions and sensor platforms. These results provide a systematic evaluation of foundation models for operational hyperspectral crop mapping and outline directions for future model development.