I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Unsupervised Detection of ASH Dieback Disease (Hymenoscyphu..:
, In:
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
,
Polk, Sam L.
;
Chan, Aland H. Y.
;
Cui, Kangning
... - p. 2287-2290 , 2022
Link:
https://doi.org/10.1109/IGARSS46834.2022.9883429
RT T1 <cTitle>IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium</cTitle>: T1 <Title>Unsupervised Detection of ASH Dieback Disease (Hymenoscyphus Fraxineus) Using Diffusion-Based Hyperspectral Image Clustering</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9883429&Exemplar=1&LAN=DE A1 Polk, Sam L. A1 Chan, Aland H. Y. A1 Cui, Kangning A1 Plemmons, Robert J. A1 Coomes, David A. A1 Murphy, James M. YR 2022 SN 2153-7003 K1 Radio frequency K1 Pipelines K1 Clustering algorithms K1 Ash K1 Forestry K1 Machine learning K1 Labeling K1 Clustering K1 Diffusion K1 Graphs K1 Hyperspectral Images K1 Unsupervised Machine Learning SP 2287 OP 2290 LK http://dx.doi.org/https://doi.org/10.1109/IGARSS46834.2022.9883429 DO https://doi.org/10.1109/IGARSS46834.2022.9883429 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
