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
Tropical Forests Mapping with Tandem-X and Deep Learning Me..:
, In:
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
,
Bueso-Bello, Jose-Luis
;
Carcereri, Daniel
;
Martone, Michele
.. - p. 5684-5687 , 2022
Link:
https://doi.org/10.1109/IGARSS46834.2022.9883995
RT T1 <cTitle>IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium</cTitle>: T1 <Title>Tropical Forests Mapping with Tandem-X and Deep Learning Methods</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9883995&Exemplar=1&LAN=DE A1 Bueso-Bello, Jose-Luis A1 Carcereri, Daniel A1 Martone, Michele A1 Gonzalez, Carolina A1 Rizzoli, Paola YR 2022 SN 2153-7003 K1 Deep learning K1 Training K1 Geometry K1 Asia K1 Geoscience and remote sensing K1 Clustering algorithms K1 Forestry K1 Synthetic Aperture Radar K1 TanDEM-X K1 rainforest K1 tropical forest K1 forest mapping K1 deforestation monitoring K1 deep learning K1 convolutional neural network K1 U-Net SP 5684 OP 5687 LK http://dx.doi.org/https://doi.org/10.1109/IGARSS46834.2022.9883995 DO https://doi.org/10.1109/IGARSS46834.2022.9883995 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
