Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.3390/land11112039
Lizenz creative commons licence
Titel (primär) Coastal wetland vegetation classification using pixel-based, object-based and deep learning methods based on RGB-UAV
Autor Zheng, J.-Y.; Hao, Y.-Y.; Wang, Y.-C.; Zhou, S.-Q.; Wu, W.-B.; Yuan, Q.; Gao, Y.; Guo, H.-Q.; Cai, X.-X.; Zhao, B.
Quelle Land
Erscheinungsjahr 2022
Department SUSOZ
Band/Volume 11
Heft 11
Seite von art. 2039
Sprache englisch
Topic T5 Future Landscapes
Keywords coastal wetlands; unmanned aerial vehicles; vegetation classification; deep learning; object-based image analysis (OBIA); Google Earth Engine (GEE)
Abstract The advancement of deep learning (DL) technology and Unmanned Aerial Vehicles (UAV) remote sensing has made it feasible to monitor coastal wetlands efficiently and precisely. However, studies have rarely compared the performance of DL with traditional machine learning (Pixel-Based (PB) and Object-Based Image Analysis (OBIA) methods) in UAV-based coastal wetland monitoring. We constructed a dataset based on RGB-based UAV data and compared the performance of PB, OBIA, and DL methods in the classification of vegetation communities in coastal wetlands. In addition, to our knowledge, the OBIA method was used for the UAV data for the first time in this paper based on Google Earth Engine (GEE), and the ability of GEE to process UAV data was confirmed. The results showed that in comparison with the PB and OBIA methods, the DL method achieved the most promising classification results, which was capable of reflecting the realistic distribution of the vegetation. Furthermore, the paradigm shifts from PB and OBIA to the DL method in terms of feature engineering, training methods, and reference data explained the considerable results achieved by the DL method. The results suggested that a combination of UAV, DL, and cloud computing platforms can facilitate long-term, accurate monitoring of coastal wetland vegetation at the local scale.
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=26176
Zheng, J.-Y., Hao, Y.-Y., Wang, Y.-C., Zhou, S.-Q., Wu, W.-B., Yuan, Q., Gao, Y., Guo, H.-Q., Cai, X.-X., Zhao, B. (2022):
Coastal wetland vegetation classification using pixel-based, object-based and deep learning methods based on RGB-UAV
Land 11 (11), art. 2039 10.3390/land11112039