SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery

• URL: http://arxiv.org/abs/2506.06176v1
• Date: Fri, 06 Jun 2025 15:39:54 GMT
• Title: SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery
• Authors: Zhenyu Yu, Mohd. Yamani Idna Idris, Pei Wang, Yuelong Xia, Fei Ma, Rizwan Qureshi,
• Abstract summary: We propose a novel symbolic regression framework that derives physically interpretable expressions directly from remote sensing imagery.<n>SatelliteFormula combines a Vision Transformer-based encoder for spatial-spectral feature extraction with physics-guided constraints to ensure consistency and interpretability.
• Score: 8.965479246496878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose SatelliteFormula, a novel symbolic regression framework that derives physically interpretable expressions directly from multi-spectral remote sensing imagery. Unlike traditional empirical indices or black-box learning models, SatelliteFormula combines a Vision Transformer-based encoder for spatial-spectral feature extraction with physics-guided constraints to ensure consistency and interpretability. Existing symbolic regression methods struggle with the high-dimensional complexity of multi-spectral data; our method addresses this by integrating transformer representations into a symbolic optimizer that balances accuracy and physical plausibility. Extensive experiments on benchmark datasets and remote sensing tasks demonstrate superior performance, stability, and generalization compared to state-of-the-art baselines. SatelliteFormula enables interpretable modeling of complex environmental variables, bridging the gap between data-driven learning and physical understanding.

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