학술논문
Delineation of Bare Soil Field Areas from Unmanned Aircraft System Imagery with the Mean Shift Unsupervised Clustering and the Random Forest Supervised Classification
Document Type
article
Author
Source
Canadian Journal of Remote Sensing, Vol 46, Iss 4, Pp 489-500 (2020)
Subject
Language
English
French
French
ISSN
1712-7971
07038992
07038992
Abstract
The use of aerial remote sensing platforms such as Unmanned Aircraft Systems (UAS) has been proven as a cost and time effective way to perform tasks related to precision agriculture and decision making. Two machine learning (ML) algorithms have been implemented on UAS blue and red band imagery to delineate field areas and extents of various bare soil fields: the Random Forest non-parametric supervised classifier and the unsupervised non-parametric Mean Shift clustering algorithm. Both ML algorithms perform exceptionally well. The mean Area Goodness of Fit (AGoF) for bare soil areas was greater than 99% and the mean Boundary Mean Positional Error (BMPE) was lower than 0.6 m for the 11 surveyed fields. Such precisions with ML algorithms will enable an easy automated field boundary delineation based on UAS imagery. Such information is needed by growers and crop insurance agencies for an accurate determination of the crop insurance premiums.