Identifying plant species in kettle holes using UAV images and deep learning techniques
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
Abstract
The use of uncrewed aerial vehicle to map the environment increased significantly in the last decade enabling a finer assessment of the land cover. However, creating accurate maps of the environment is still a complex and costly task. Deep learning (DL) is a new generation of artificial neural network research that, combined with remote sensing techniques, allows a refined understanding of our environment and can help to solve challenging land cover mapping issues. This research focuses on the vegetation segmentation of kettle holes. Kettle holes are small, pond-like, depressional wetlands. Quantifying the vegetation present in this environment is essential to assess the biodiversity and the health of the ecosystem. A machine learning workflow has been developed, integrating a superpixel segmentation algorithm to build a robust dataset, which is followed by a set of DL architectures to classify 10 plant classes present in kettle holes. The best architecture for this task was Xception, which achieved an average F1-score of 85% in the segmentation of the species. The application of solely 318 samples per class enabled a successful mapping in the complex wetland environment, indicating an important direction for future health assessments in such landscapes.
Details
Originalsprache | Englisch |
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Seiten (von - bis) | 1-16 |
Seitenumfang | 16 |
Fachzeitschrift | Remote Sensing in Ecology and Conservation |
Jahrgang | 9 |
Ausgabenummer | 1 |
Publikationsstatus | Veröffentlicht - Feb. 2023 |
Peer-Review-Status | Ja |
Externe IDs
Scopus | 85135857394 |
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WOS | 000839367100001 |
Mendeley | 88a78049-8c51-3171-9bf4-0e90174c0a3a |
unpaywall | 10.1002/rse2.291 |
Schlagworte
Ziele für nachhaltige Entwicklung
ASJC Scopus Sachgebiete
Schlagwörter
- Deep learning, image segmentation, plant species segmentation, superpixels, uncrewed aerial vehicle, wetland, Image segmentation, Uncrewed aerial vehicle, Plant species segmentation, Superpixels, Wetland