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

• Autorzy: Taha Lamyaa Gamal El-deen , Ibrahim Rania Elsayed

Identyfikatory

DOI

10.7494/geom.2021.15.4.101

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

ensemble classifiers; machine learning; random forest; maximum likelihood; support vector machine; backpropagation; image classification

PL

klasyfikatory zespołowe; uczenie maszynowe; las losowy; maksymalne prawdopodobieństwo; maszyny wektorów podporowych; backpropagation; klasyfikacja obrazów

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2021
• Tom: Vol. 15, no. 4
• Strony: 101--116
• Opis fizyczny: Bibliogr. 48 poz., fot., tab.

Twórcy

autor

Taha Lamyaa Gamal El-deen

• National Authority for Remote Sensing and Space Sciences, Cairo, Egypt

• https://orcid.org/0000-0003-2890-2498

autor

Ibrahim Rania Elsayed

• National Authority for Remote Sensing and Space Sciences, Cairo, Egypt

• https://orcid.org/0000-0001-5116-5866

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)