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dc.contributor.authorBorra-Serrano, Irene
dc.contributor.authorPeña, José Manuel
dc.contributor.authorTorres-Sánchez, Jorge
dc.contributor.authorMesas Carrascosa, Francisco Javier
dc.contributor.authorLópez-Granados, Francisca
dc.date.accessioned2017-11-06T12:53:51Z
dc.date.available2017-11-06T12:53:51Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10396/15311
dc.description.abstractUnmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightshttps://creativecommons.org/licenses/by/4.0/es_ES
dc.sourceSensors 15(8), 19688-19708 (2015)es_ES
dc.subjectUAVes_ES
dc.subjectOrtho-mosaicked imagees_ES
dc.subjectResamplinges_ES
dc.subjectOBIAes_ES
dc.subjectWeed mappinges_ES
dc.subjectVisible (RGB)es_ES
dc.subjectNear-infrared (NIR)es_ES
dc.titleSpatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mappinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttp://dx.doi.org/10.3390/s150819688es_ES
dc.relation.projectIDGobierno de España. RECUPERA-2020es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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