1D-CNN FOR LAND COVER CLASSIFICATION OF SENTINEL-3 ALTIMETRY WAVEFORMS USING ADDITIONAL FEATURES

被引:0
|
作者
Eitel, Maximilian [1 ]
Schmitt, Michael [1 ]
机构
[1] Univ Bundeswehr Munich, Dept Aerosp Engn, Munich, Germany
来源
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年
关键词
SAR-Altimetry; Land Cover Classification; Sentinel-3; 1D-CNN;
D O I
10.1109/IGARSS52108.2023.10283392
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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.
引用
收藏
页码:3058 / 3061
页数:4
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