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Sensors 2017, 17(9), 2007; https://doi.org/10.3390/s17092007

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images

1
Laboratory of Remote Sensing and GIS, Faculty of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
2
Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
3
Plant Pathology Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
4
USDA-ARS-European Biological Control Laboratory, Tsimiski 43, 7th floor, Thessaloniki 54623, Greece
5
Geosense S.A., Filikis Etairias 15-17, Pylaia, Thessaloniki 55535, Greece
*
Author to whom correspondence should be addressed.
Received: 28 July 2017 / Revised: 23 August 2017 / Accepted: 28 August 2017 / Published: 1 September 2017
(This article belongs to the Special Issue Sensors in Agriculture)
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Abstract

In 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
Keywords: weeds; UAS; RPAS; one-class; machine learning; remote sensing; geoinformatics weeds; UAS; RPAS; one-class; machine learning; remote sensing; geoinformatics
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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.

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