E3S Web Conf.
Volume 364, 2023The 3ʳᵈ Edition of Oriental Days for the Environment “Green Lab. Solution for Sustainable Development” (JOE3)
|Number of page(s)
|Green Technology & Sustainable Development
|23 January 2023
Classification of a quickbird satellite image by Machine learning techniques: Mapping an urban Environement by decision tree method
1 Remote Sensing and Geographic Information Systems Applied to Geosciences and Environment Laboratory, Faculty of Science and Technology, Beni Mellal, Morocco
2 Bio-processes and Bio-interface Laboratory, Faculty of Science and Technology, Beni Mellal, Morocco
* Corresponding author: firstname.lastname@example.org
Classification is a crucial stage in the processing of satellite images that influence considerably the quality of the result. A variety of methods is proposed in the literature for the purposes of image classification. They present many differences in their basic principles, thus in the quality of the results obtained. Therefore, a study of different classification methods seems to be essential. The classification of satellite images with conventional methods can be done in several ways using different algorithms. These algorithms can be divided into two main categories: supervised and non-supervised. Decision tree on the contrary is a machine learning tool. It is a plain model characterized by the simplicity of understanding and interpretation. This work aims firstly, to classify a high resolution Quickbird satellite image of an urban area by the decision tree method and compare it with the conventional classification algorithms in order to evaluate its efficiency. The methodology consists of two main stages: classification and evaluation of results. The second is based on the calculation of a number of statistical indices derived from the confusion matrix: the statistical parameter “kappa’ and the overall coefficient of precision.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.