Geomatics and Environmental Engineering, vol. 15, no. 4

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Vol. 15 No. 4 (2021): Geomatics and Environmental Engineering

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Submitted
May 6, 2021
Accepted
Sep 22, 2021
Published
Oct 8, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

https://doi.org/10.7494/geom.2021.15.4.101
Lamyaa Gamal El-deen Taha
National Authority for Remote Sensing and Space Sciences, Cairo, Egypt
https://orcid.org/0000-0003-2890-2498
Rania Elsayed Ibrahim
National Authority for Remote Sensing and Space Sciences, Cairo, Egypt
http://orcid.org/0000-0001-5116-5866

Corresponding Author(s) : Rania Elsayed Ibrahim

ranyaalsayed@gmail.com

Geomatics and Environmental Engineering, Vol. 15 No. 4 (2021): Geomatics and Environmental Engineering

Abstract

The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery.
The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.

Keywords

ensemble classifiers machine learning random forest maximum likelihood support vector machines backpropagation image classification
Taha, L. G. E.- deen, & Ibrahim, R. E. (2021). Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images. Geomatics and Environmental Engineering, 15(4), 101–116. https://doi.org/10.7494/geom.2021.15.4.101
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