@conference{UAVandOBIAPerez2015,
author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and J.M. Pe{\~n}a and J. Torres-S{\'a}nchez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and F. L{\'o}pez Granados",
abstract = "Weed control in precision agriculture refers to the design of site-specific control treatments according to weed coverage and it is very useful to minimise costs and environmental risks. The crucial component is to provide precise and timely weed maps via weed monitoring. This paper compares different approaches for weed mapping using imagery from Unmanned Aerial Vehicles in sunflower crops. We explore differ- ent alternatives, such as object-based analysis, which is a strategy that is spreading rapidly in the field of remote sensing. The usefulness of these approaches is tested by considering support vector machines, one of the most popular machine learning classifiers. The results show that the object-based analysis is more promising than the pixel-based one and demonstrate that both the features related to vegetation indexes and those related to the shape of the objects are meaningful for the problem. ",
booktitle = "13th International Work-Conference on Artificial Neural Networks (IWANN 2015)",
isbn = "978-3-319-19257-4",
month = "10th-12th June",
organization = "Palma de Mallorca (Spain)",
pages = "252--262",
publisher = "Springer International Publishing",
series = "Lecture Notes in Computer Science",
title = "{A}n experimental comparison for the identification of weeds in sunflower crops via unmanned aerial vehicles and object-based analysis",
url = "http://link.springer.com/chapter/10.1007%2F978-3-319-19258-1_22",
volume = "9094",
year = "2015",
}