Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping
Autor
Borra-Serrano, Irene
Peña, José Manuel
Torres-Sánchez, Jorge
Mesas Carrascosa, Francisco Javier
López-Granados, Francisca
Editor
MDPIFecha
2015Materia
UAVOrtho-mosaicked image
Resampling
OBIA
Weed mapping
Visible (RGB)
Near-infrared (NIR)
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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.