A 2D hyperspectral library of mineral reflectance, from 900 to 2500 nm

Mineral identification using machine learning requires a significant amount of training data. We built a library of 2D hyperspectral images of minerals. The library contains reflectance images of 130 samples, of 76 distinct minerals, with more than 3.9 million data points. In order to produce this d...

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Detalles Bibliográficos
Autores principales: Fasnacht, Laurent, Vogt, Marie-Louise, Renard, Philippe, Brunner, Philip
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Data Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6848079/
https://www.ncbi.nlm.nih.gov/pubmed/31712558
http://dx.doi.org/10.1038/s41597-019-0261-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6848079/
https://www.ncbi.nlm.nih.gov/pubmed/31712558
http://dx.doi.org/10.1038/s41597-019-0261-9

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