Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource, 1 online resource.

Language: Englisch

Bibliographic citation: 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

Creator: Qamar, Farid
Dobler, Gregory

Contributor: SpringerLink (Online service)

DOI: 10.1186/s13007-023-01046-6

URN: urn:nbn:de:101:1-2023103111300527024605

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 11:01 AM CEST

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Qamar, Farid
Dobler, Gregory
SpringerLink (Online service)

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Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing

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DROACOR<sup>®</sup> REFLECTANCE RETRIEVAL FOR HYPERSPECTRAL MINERAL EXPLORATION USING A GROUND-BASED ROTATING PLATFORM

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