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Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping

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Author
Borra-Serrano, Irene
Peña, José Manuel
Torres-Sánchez, Jorge
Mesas Carrascosa, Francisco Javier
López-Granados, Francisca
Publisher
MDPI
Date
2015
Subject
UAV
Ortho-mosaicked image
Resampling
OBIA
Weed mapping
Visible (RGB)
Near-infrared (NIR)
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Abstract
Unmanned 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.
URI
http://hdl.handle.net/10396/15311
Fuente
Sensors 15(8), 19688-19708 (2015)
Versión del Editor
http://dx.doi.org/10.3390/s150819688
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