Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images
AbstractIn the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery. View Full-Text
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Alexandridis, T.K.; Tamouridou, A.A.; Pantazi, X.E.; Lagopodi, A.L.; Kashefi, J.; Ovakoglou, G.; Polychronos, V.; Moshou, D. Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images. Sensors 2017, 17, 2007.
Alexandridis TK, Tamouridou AA, Pantazi XE, Lagopodi AL, Kashefi J, Ovakoglou G, Polychronos V, Moshou D. Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images. Sensors. 2017; 17(9):2007.Chicago/Turabian Style
Alexandridis, Thomas K.; Tamouridou, Afroditi Alexandra; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L.; Kashefi, Javid; Ovakoglou, Georgios; Polychronos, Vassilios; Moshou, Dimitrios. 2017. "Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images." Sensors 17, no. 9: 2007.