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...

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Detalles Bibliográficos
Autores principales: Zeegers, Mathé T., Pelt, Daniël M., van Leeuwen, Tristan, van Liere, Robert, Batenburg, Kees Joost
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321191/
https://www.ncbi.nlm.nih.gov/pubmed/34460529
http://dx.doi.org/10.3390/jimaging6120132

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