@inproceedings{,
    author = {Eitel, Maximilian; Schmitt, Michael},
    title = {1D-CNN for land cover classification of Sentinel-3 altimetry waveforms using additional features},
    editor = {},
    booktitle = {IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium},
    series = {},
    journal = {},
    address = {Piscataway, NJ},
    publisher = {IEEE},
    edition = {},
    year = {2023},
    isbn = {979-8-3503-2010-7},
    volume = {},
    number =  {},
    pages = {3058-3061},
    url = {https://ieeexplore.ieee.org/document/10283392},
    doi = {10.1109/IGARSS52108.2023.10283392},
    keywords = {},
    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 peculiarities of altimetry waveforms. To evaluate the performance of our approach, we employ the F1-score metric, which provides a balanced measure of precision and recall. Our experimental results demonstrate a notable F1-score of 0.86, indicating the effectiveness of our proposed method in accurately classifying land cover types from altimetry data.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Luft- und Raumfahrttechnik, LRT 9 - Institut für Raumfahrttechnik und Weltraumnutzung, Professur: Schmitt, Michael},
    
    
}