Novelty detection classifiers in weed mapping: Silybum marianum detection on UAV multispectral images

Sensors (Switzerland)

Volumn 17, Issue 9, 2017, Pages

  Alexandridis, Thomas K ^a   Tamouridou, Afroditi Alexandra ^a   Pantazi, Xanthoula Eirini ^a   Lagopodi, Anastasia L ^a   Kashefi, Javid ^b   Ovakoglou, Georgios ^a   Polychronos, Vassilios ^c   Moshou, Dimitrios ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b USDA ARS European Biological Control Laboratory   (Greece)

^c Geosense SA   (Greece)

Author keywords

Geoinformatics; Machine learning; One class; Remote sensing; RPAS; UAS; Weeds

Indexed keywords

CONFORMAL MAPPING; FIXED WINGS; IMAGE CLASSIFICATION; LEARNING SYSTEMS; MAPPING; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SELF ORGANIZING MAPS; SUPPORT VECTOR MACHINES; UNMANNED AERIAL VEHICLES (UAV);

GEO-INFORMATICS; HIGH RESOLUTION IMAGE; IDENTIFICATION ACCURACY; MULTI-SPECTRAL CAMERAS; ONE-CLASS; ONE-CLASS SUPPORT VECTOR MACHINE; RPAS; WEEDS;

AIRCRAFT DETECTION;

ARTICLE; AUTOENCODER; FEASIBILITY STUDY; HUMAN; HUMAN EXPERIMENT; IMAGERY; NONHUMAN; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SILYBUM MARIANUM; SUPPORT VECTOR MACHINE; VEGETATION; WEED; WING;

EID: 85028708486     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s17092007     Document Type: Article

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