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| 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 | 1033761 | ||
| 005 | 20250203103252.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2024-06602 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-06602 |
| 100 | 1 | _ | |a Buffat, Jim |0 P:(DE-Juel1)188104 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a German Conference on Pattern Recognition 2024 |g GCPR 2024 |c Munich |d 2024-09-10 - 2024-09-13 |w Germany |
| 245 | _ | _ | |a Deep Learning Based Prediction of Sun-Induced Fluorescence from HyPlant Imagery |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1734086410_7857 |2 PUB:(DE-HGF) |x After Call |
| 500 | _ | _ | |a Poster presented as part of the Nectar Track |
| 502 | _ | _ | |c University of Bonn |
| 520 | _ | _ | |a The retrieval of sun-induced fluorescence (SIF) from hyperspectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements 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. |
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| 700 | 1 | _ | |a Pato, Miguel |0 P:(DE-HGF)0 |b 1 |
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| 700 | 1 | _ | |a Maier, Stefan |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Müller, Rupert |0 P:(DE-HGF)0 |b 6 |
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| 700 | 1 | _ | |a Scharr, Hanno |0 P:(DE-Juel1)129394 |b 9 |u fzj |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1033761/files/FZJ_Poster_hoch.pdf |y OpenAccess |
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