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Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning
An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not take prior knowledge about the task into account. Consequently, sparsely occurring features that may be essential for the imaging task may not be pre...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321191/ https://www.ncbi.nlm.nih.gov/pubmed/34460529 http://dx.doi.org/10.3390/jimaging6120132 |