Hoppe, H.; Dietrich, P.; Marzahn, P.; Weiß, T.; Nitzsche, C.; Freiherr von Lukas, U.; Wengerek, T.; Borg, E.
Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences. Remote Sens. 2024, 16, 1493.
https://doi.org/10.3390/rs16091493
AMA Style
Hoppe H, Dietrich P, Marzahn P, Weiß T, Nitzsche C, Freiherr von Lukas U, Wengerek T, Borg E.
Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences. Remote Sensing. 2024; 16(9):1493.
https://doi.org/10.3390/rs16091493
Chicago/Turabian Style
Hoppe, Hauke, Peter Dietrich, Philip Marzahn, Thomas Weiß, Christian Nitzsche, Uwe Freiherr von Lukas, Thomas Wengerek, and Erik Borg.
2024. "Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences" Remote Sensing 16, no. 9: 1493.
https://doi.org/10.3390/rs16091493
APA Style
Hoppe, H., Dietrich, P., Marzahn, P., Weiß, T., Nitzsche, C., Freiherr von Lukas, U., Wengerek, T., & Borg, E.
(2024). Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences. Remote Sensing, 16(9), 1493.
https://doi.org/10.3390/rs16091493
