Learning from hyperspectral remote sensing data for machine learning algorithms in earth science
| dc.contributor.author | Christoph Jörges, Sandra Dotzler | |
| dc.date.accessioned | 2024-04-08T11:56:34Z | |
| dc.date.available | 2024-04-08T11:56:34Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Machine Learning in Earth sciences heavily depends on sufficient training data for proper generalization. Since in-situ ground truth data is rarely available and cost-intensive to obtain, this study presents a new approach of deriving training data from hyperspectral remote sensing satellites by physical spectral signatures to use them for data-driven models with operationally available multispectral data. Examples include monitoring of crop rotation, winter greening, soil organic matter, and detection of plastic covered greenhouses (PCGs). | en |
| dc.identifier.isbn | 978-3-88579-738-8 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/43892 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | 44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft | |
| dc.relation.ispartofseries | Lecture Notes in Informatics(LNI) - Proceedings, Volume P - 344 | |
| dc.subject | hyperspectral imaging | |
| dc.subject | plasticulture | |
| dc.subject | machine learning | |
| dc.subject | land use classification | |
| dc.subject | remote sensing | |
| dc.title | Learning from hyperspectral remote sensing data for machine learning algorithms in earth science | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 310 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 305 | |
| gi.conference.date | 27.-28. Februar 2058 | |
| gi.conference.location | Stuttgart | |
| gi.conference.review | full |
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