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Authors:
Eitel, Maximilian; Schmitt, Michael 
Document type:
Konferenzbeitrag / Conference Paper 
Title:
1D-CNN for land cover classification of Sentinel-3 altimetry waveforms using additional features 
Title of conference publication:
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium 
Organizer (entity):
IEEE 
Conference title:
International Geoscience and Remote Sensing Symposium (2023, Pasadena, Calif.) 
Venue:
Pasadena, CA, USA 
Year of conference:
2023 
Date of conference beginning:
16.07.2023 
Date of conference ending:
21.07.2023 
Place of publication:
Piscataway, NJ 
Publisher:
IEEE 
Year:
2023 
Pages from - to:
3058-3061 
Language:
Englisch 
Abstract:
In this research, we focus on the classification of land cover types using radar altimetry data and evaluate the sensitivity of the altimetry signal across different land cover categories. To perform the classification task, we create a comprehensive dataset by combining altimetry footprints and the ESA World- cover2020 dataset. To model the classification, we employ multiple 1D-CNN (Convolutional Neural Network) architectures originally developed for other applications and adapt them to the p...    »
 
ISBN:
979-8-3503-2010-7 
Department:
Fakultät für Luft- und Raumfahrttechnik 
Institute:
LRT 9 - Institut für Raumfahrttechnik und Weltraumnutzung 
Chair:
Schmitt, Michael 
Open Access yes or no?:
Nein / No