Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling



This thesis addresses the environmental uncertainty in satellite images as a computer vision task using semantic image segmentation. We focus in the reduction of the error caused by the use of a single-environment models in wireless communications. We propose to use computer vision and image analysis to segment a geographical terrain in order to employ a specific propagation model in each segment of the link. Our computer vision architecture achieved a segmentation accuracy of 89.41%, 86.47%, and 87.37% in the urban, suburban, and rural classes, respectively. Results indicate that estimating propagation loss with our multi-environment model reduced the root mean square deviation (RMSD) with respect to two publicly available tracing datasets.


Computer Vision, Scene Understanding, Pattern Recognition, Separation and Segmentation, Applications, Machine Vision, Other applications

Author Biography

Manuel Eugenio Morocho-Cayamcela, Escuela Superior Politécnica del Litoral

Received the B.S. degree in electronic engineering from Universidad Politécnica Salesiana, Cuenca, Ecuador, in 2012, the M.Sc. degree in communications engineering and networks from The University of Birmingham, England, United Kingdom, in 2016, and the Ph.D. in electronic engineering at Kumoh National Institute of Technology, Gumi, South Korea. From 2017 to 2020, he has been a Research Assistant with KIT Future Communications and Systems Laboratory. He is currently working as a researcher at Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador.
His research interests include computer vision and image analysis, artificial intelligence, signal processing, statistical analysis, and optimization techniques. Mr. Morocho-Cayamcela was a recipient of the SENESCYT Fellowship from The National Secretariat for Higher Education, Science, Technology and Innovation of Ecuador in 2015, the KIT Doctoral Grant from Kumoh National Institute of Technology in 2017.




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