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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.06176 |
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| _version_ | 1866915331478388736 |
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| author | Yu, Zhenyu Idris, Mohd. Yamani Idna Wang, Pei Xia, Yuelong Ma, Fei Qureshi, Rizwan |
| author_facet | Yu, Zhenyu Idris, Mohd. Yamani Idna Wang, Pei Xia, Yuelong Ma, Fei Qureshi, Rizwan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06176 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery Yu, Zhenyu Idris, Mohd. Yamani Idna Wang, Pei Xia, Yuelong Ma, Fei Qureshi, Rizwan Computer Vision and Pattern Recognition 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. |
| title | SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.06176 |