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Sensors 2015, 15(8), 19688-19708;

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

Institute for Sustainable Agriculture, IAS-CSIC, P.O. Box 4084, Córdoba 14080, Spain
Department of Graphic Engineering and Geomatics, Campus de Rabanales, University of Cordoba, Córdoba 14071, Spain
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 12 May 2015 / Revised: 2 July 2015 / Accepted: 31 July 2015 / Published: 12 August 2015
(This article belongs to the Section Remote Sensors)
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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. View Full-Text
Keywords: UAV; ortho-mosaicked image; resampling; OBIA; weed mapping; visible (RGB); near-infrared (NIR) UAV; ortho-mosaicked image; resampling; OBIA; weed mapping; visible (RGB); near-infrared (NIR)

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Borra-Serrano, I.; Peña, J.M.; Torres-Sánchez, J.; Mesas-Carrascosa, F.J.; López-Granados, F. Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping. Sensors 2015, 15, 19688-19708.

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