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Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing
BACKGROUND: Vegetation spectral reflectance obtained with hyperspectral imaging (HSI) offer non-invasive means for the non-destructive study of their physiological status. The light intensity at visible and near-infrared wavelengths (VNIR, 0.4–1.0µm) captured by the sensor are composed of mixtures o...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2023
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385980/ https://www.ncbi.nlm.nih.gov/pubmed/37516859 http://dx.doi.org/10.1186/s13007-023-01046-6 |