Benchmarking foundation models for hyperspectral image classification: Application to cereal crop type mapping

• URL: http://arxiv.org/abs/2510.11576v2
• Date: Tue, 14 Oct 2025 09:49:35 GMT
• Title: Benchmarking foundation models for hyperspectral image classification: Application to cereal crop type mapping
• Authors: Walid Elbarz, Mohamed Bourriz, Hicham Hajji, Hamd Ait Abdelali, François Bourzeix,
• Abstract summary: This study benchmarks three foundation models for cereal crop mapping using hyperspectral imagery.<n>Performance was measured with overall accuracy (OA), average accuracy (AA), and F1-score.
• Score: 0.9407085421584646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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