Evaluation of hierarchical self-organising maps for weed mapping using UAS multispectral imagery

•Three hierarchical SOMs were tested on UAV images for S. marianum weed mapping.•Challenging discrimination between vegetation species with similar spectral reflectance.•Classifiers produced high quality maps of >98% agreement with the validation dataset.•The proposed neural networks provided the...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Computers and electronics in agriculture 2017-06, Vol.139, p.224-230
Hauptverfasser:	Pantazi, X.E., Tamouridou, A.A., Alexandridis, T.K., Lagopodi, A.L., Kashefi, J., Moshou, D.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Band spectra eBee Flowers & plants High resolution Identification Image acquisition Image resolution Imagery Mapping Near infrared radiation Neural networks Precision farming Remote sensing Site-specific weed management Spectral bands Unmanned aerial vehicles Unmanned aircraft Unmanned aircraft system Vegetation
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!