Next Article in Journal
Optimal Color Lighting for Scanning Images of Flat Panel Display using Simplex Search
Previous Article in Journal
Two-Dimensional Orthonormal Tree-Structured Haar Transform for Fast Block Matching
Previous Article in Special Issue
Contribution of Remote Sensing on Crop Models: A Review
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
J. Imaging 2018, 4(11), 132; https://doi.org/10.3390/jimaging4110132

Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches

1
Laboratory of Remote Sensing, Faculty of Agriculture, Aristotle University of Thessaloniki, Spectroscopy and GIS, 541 24 Thessaloniki, Greece
2
Laboratory of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
3
Laboratory of Phytopathology, Faculty of Agriculture, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
4
Geosense S.A., Filikis Etairias 15-17, Pylaia, 555 35 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Received: 11 September 2018 / Revised: 16 October 2018 / Accepted: 2 November 2018 / Published: 9 November 2018
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
Full-Text   |   PDF [3244 KB, uploaded 9 November 2018]   |  

Abstract

Accurate mapping of weed distribution within a field is a first step towards effective weed management. The aim of this work was to improve the mapping of milk thistle (Silybum marianum) weed patches through unmanned aerial vehicle (UAV) images using auxiliary layers of information, such as spatial texture and estimated vegetation height from the UAV digital surface model. UAV multispectral images acquired in the visible and near-infrared parts of the spectrum were used as the main source of data, together with texture that was estimated for the image bands using a local variance filter. The digital surface model was created from structure from motion algorithms using the UAV image stereopairs. From this layer, the terrain elevation was estimated using a focal minimum filter followed by a low-pass filter. The plant height was computed by subtracting the terrain elevation from the digital surface model. Three classification algorithms (maximum likelihood, minimum distance and an object-based image classifier) were used to identify S. marianum from other vegetation using various combinations of inputs: image bands, texture and plant height. The resulting weed distribution maps were evaluated for their accuracy using field-surveyed data. Both texture and plant height have helped improve the accuracy of classification of S. marianum weed, increasing the overall accuracy of classification from 70% to 87% in 2015, and from 82% to 95% in 2016. Thus, as texture is easier to compute than plant height from a digital surface model, it may be preferable to be used in future weed mapping applications. View Full-Text
Keywords: milk thistle; precision farming; digital surface model; plant height; texture; Sf structure from motion milk thistle; precision farming; digital surface model; plant height; texture; Sf structure from motion
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zisi, T.; Alexandridis, T.K.; Kaplanis, S.; Navrozidis, I.; Tamouridou, A.-A.; Lagopodi, A.; Moshou, D.; Polychronos, V. Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches. J. Imaging 2018, 4, 132.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top