Gast ::
| Hauptseite > Publikationsdatenbank > DEEP LEARNING BASED PREDICTION OF SUN-INDUCED FLUORESCENCE FROM HYPLANT IMAGERY > print |
| Hauptseite > Publikationsdatenbank > DEEP LEARNING BASED PREDICTION OF SUN-INDUCED FLUORESCENCE FROM HYPLANT IMAGERY > print |
| 001 | 1018125 | ||
| 005 | 20240403082810.0 | ||
| 024 | 7 | _ | |a 10.1109/IGARSS52108.2023.10282828 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2023-04569 |2 datacite_doi |
| 024 | 7 | _ | |a WOS:001098971603050 |2 WOS |
| 037 | _ | _ | |a FZJ-2023-04569 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Buffat, Jim |0 P:(DE-Juel1)188104 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a International Geoscience and Remote Sensing Symposium |c Pasadena |d 2023-06-21 - 2023-06-21 |w USA |
| 245 | _ | _ | |a DEEP LEARNING BASED PREDICTION OF SUN-INDUCED FLUORESCENCE FROM HYPLANT IMAGERY |
| 260 | _ | _ | |c 2023 |b IEEE |
| 300 | _ | _ | |a 2993 - 2996 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1700147862_15188 |2 PUB:(DE-HGF) |
| 500 | _ | _ | |a This work is funded by the Helmholtz Initiative and Networking Fund, Helmholtz AI, Deutsches Zentrum für Luft- und Raumfahrt and Forschungszentrum Jülich GmbH. The authors gratefully acknowledge the computing time granted by the JARA Vergabegremium and provided on the JARA Partition part of the supercomputer JURECA [1] at Forschungszentrum Jülich.Jülch Supercomputing Centre, “JURECA: Data Centric and Booster Modules implementing the Modular Supercomputing Architecture at Jülch Supercomputing Centre,” Journal of large-scale research facilities, vol. 7, no. A182, 2021. [Online]. Available: http://dx.doi.org/10.17815/jlsrf-7-182 |
| 520 | _ | _ | |a The retrieval of sun-induced fluorescence (SIF) from hyper- spectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method com- bining semi-supervised deep learning with an existing spec- tral fitting method. A validation study with in-situ SIF mea- surements shows high sensitivity of the deep learning method to SIF changes even though systematic shifts deteriorate its absolute prediction accuracy. A detailed analysis of diurnal SIF dynamics and SIF prediction in topographically variable terrain highlights the benefits of this deep learning approach. |
| 536 | _ | _ | |a 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) |0 G:(DE-HGF)POF4-2173 |c POF4-217 |f POF IV |x 0 |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef Conference |
| 700 | 1 | _ | |a Pato, Miguel |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Alonso, Kevin |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Auer, Stefan |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Carmona, Emiliano |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Maier, Stefan |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Müller, Rupert |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Rademske, Patrick |0 P:(DE-Juel1)162306 |b 7 |
| 700 | 1 | _ | |a Rascher, Uwe |0 P:(DE-Juel1)129388 |b 8 |
| 700 | 1 | _ | |a Scharr, Hanno |0 P:(DE-Juel1)129394 |b 9 |
| 773 | _ | _ | |a 10.1109/IGARSS52108.2023.10282828 |y 2023 |
| 856 | 4 | _ | |u https://ieeexplore.ieee.org/document/10282828 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1018125/files/igarss2023.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1018125 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)188104 |
| 910 | 1 | _ | |a DLR, Remote Sensing Technology Institute |0 I:(DE-HGF)0 |b 1 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a RHEA Group c/o European Space Agency (ESA) |0 I:(DE-HGF)0 |b 2 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a DLR, Remote Sensing Technology Institute |0 I:(DE-HGF)0 |b 3 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a DLR, Remote Sensing Technology Institute |0 I:(DE-HGF)0 |b 4 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a DLR, Remote Sensing Technology Institute |0 I:(DE-HGF)0 |b 5 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a DLR, Remote Sensing Technology Institute |0 I:(DE-HGF)0 |b 6 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)162306 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 8 |6 P:(DE-Juel1)129388 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 9 |6 P:(DE-Juel1)129394 |
| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Erde und Umwelt |l Erde im Wandel – Unsere Zukunft nachhaltig gestalten |1 G:(DE-HGF)POF4-210 |0 G:(DE-HGF)POF4-217 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-200 |4 G:(DE-HGF)POF |v Für eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten |9 G:(DE-HGF)POF4-2173 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 1 |
| 914 | 1 | _ | |y 2023 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l no |
| 920 | 1 | _ | |0 I:(DE-Juel1)IBG-2-20101118 |k IBG-2 |l Pflanzenwissenschaften |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-8-20210421 |k IAS-8 |l Datenanalyse und Maschinenlernen |x 1 |
| 980 | _ | _ | |a contrib |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)IBG-2-20101118 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-8-20210421 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|