Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource, 1 online resource.
- Sprache
-
Englisch
- Erschienen in
-
Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing ; volume:19 ; number:1 ; day:29 ; month:7 ; year:2023 ; pages:1-21 ; date:12.2023
Plant methods ; 19, Heft 1 (29.7.2023), 1-21, 12.2023
- Urheber
-
Qamar, Farid
Dobler, Gregory
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1186/s13007-023-01046-6
- URN
-
urn:nbn:de:101:1-2023103111300527024605
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 11:01 MESZ
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Beteiligte
- Qamar, Farid
- Dobler, Gregory
- SpringerLink (Online service)
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