Paper Info
Title
Comparison of Supervised Classifiers and Image Features for Crop Rows Segmentation on Aerial Images
Abstract
In this paper we present a comparison of supervised classifiers and image features for crop row segmentation of aerial images captured from an unmanned aerial vehicle (UAV). The main goal is to investigate which methods are the most suitable to solve this specific problem, as well as to test quantitatively how well they perform for robust segmentation of row patterns. For this purpose, we conducted a systematic literature review over the recent methods specifically designed for aerial image crop row segmentation, and for comparison purposes we implemented the most prominent approaches. Most used Color-texture features were faced against most used classifiers, resulting into a total of 48 combinations, usually having their construction concepts based on the following two step-procedures: (i) supervised training step to build some model over the selected color-texture feature space which is also based upon user-selected samples from the input image; and (ii) classification step, where each pixel of the input image is classified employing the corresponding classifier. The obtained results were compared against a Ground-Truth (GT) image, performed by a human expert, using two distinct evaluation metrics, indicating the most suitable combination of color-texture descriptors and classifiers able to solve the segmentation problem of specific cultures obtained from UAV images.
Year
DOI
Venue
2020
10.1080/08839514.2020.1720131
APPLIED ARTIFICIAL INTELLIGENCE
DocType
Volume
Issue
Journal
34.0
4
ISSN
Citations 
PageRank 
0883-9514
1
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Paulo César Pereira Júnior110.34
Alexandre Monteiro210.34
Rafael Da Luz Ribeiro310.34
Antonio Carlos Sobieranski4114.23
Aldo von Wangenheim520949.44