scholarly journals Support Vector Machines for Object Based Building Extraction in Suburban Area using Very High Resolution Satellite Images, a Case Study: Tetuan, Morocco

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Omar Benarchid ◽  
Naoufal Raissouni
Keyword(s):  
Support Vector Machines ◽  
High Resolution ◽  
Satellite Images ◽  
Support Vector ◽  
Building Extraction ◽  
Object Based ◽  
High Resolution Satellite Images ◽  
Vector Machines ◽  
Very High

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

2021 ◽  
Vol 15 (4) ◽  
pp. 101-116
Author(s):  
Lamyaa Gamal El-deen Taha ◽  
Rania Elsayed Ibrahim
Keyword(s):  
Support Vector Machines ◽  
Maximum Likelihood ◽  
High Resolution ◽  
Random Forest ◽  
Satellite Images ◽  
Support Vector ◽  
Morphological Operations ◽  
Vector Machines ◽  
High Resolution Images ◽  
Very High

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.

An Approach for Building Rooftop Border Extraction from Very High-Resolution Satellite Images

2021 ◽  
pp. 1-11
Author(s):  
Yasser Mostafa ◽  
Mahmoud Nokrashy O. Ali ◽  
Faten Mostafa ◽  
Mohamed Yousef
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
High Resolution Satellite Images ◽  
Very High
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