Low-High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification

Literals

dcterms:contributor
- Haut, J.M., Bernabé, S., Paoletti, M.E., Fernandez-Beltran, R., Plaza, A., Plaza, J.
dcterms:creator
- Haut J.M.
ou:bibtex
- @article{Paoletti2019,title = {Low-High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification},journal = {IEEE Geoscience and Remote Sensing Letters},year = {2019},volume = {16},number = {5},pages = {776-780},author = {Haut, J.M. and Bernab{\'e}, S. and Paoletti, M.E. and Fernandez-Beltran, R. and Plaza, A. and Plaza, J.}}
bibo:doi
- 10.1109/lgrs.2018.2881045
bibo:eissn
- 1558-0571
ou:eid
- 2-s2.0-85057874619
dcterms:publisher
- IEEE Geoscience and Remote Sensing Letters
bibo:page_range
- 776-780
vivo:identifier
- 2019-268
fabio:hasPublicationYear
- 2019
bibo:issn
- 1545-598X
ou:tipoPublicacion
- Article
dcterms:title
- Low-High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification
vcard:url
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85057874619&origin=inward
ou:urlOrcid
- http://www.scopus.com/inward/record.url?eid=2-s2.0-85057874619&partnerID=MN8TOARS
ou:urlScopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85057874619&origin=inward
ou:vecesCitado
- 35
bibo:volume
- 16