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

Sensors

Irene Borra-Serrano²  José Manuel Pe༚²  Jorge Torres-Sánchez²  Francisco Javier Mesas-Carrascosa¹  Francisca López-Granados² 

[1] Department of Graphic Engineering and Geomatics, Campus de Rabanales, University of Cordoba, Córdoba 14071, Spain; E-Mail:;Institute for Sustainable Agriculture, IAS-CSIC, P.O. Box 4084, Córdoba 14080, Spain; E-Mails:

关键词: UAV;    ortho-mosaicked image;    resampling;    OBIA;    weed mapping;    visible (RGB);    near-infrared (NIR);   

DOI  :  10.3390/s150819688

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.