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

ELCVIA Electronic Letters on Computer Vision and Image Analysis

Manuel Eugenio Morocho-Cayamcela¹ 

[1] Escuela Superior Politécnica del Litoral;

关键词: Computer Vision;    Scene Understanding;    Pattern Recognition;    Separation and Segmentation;    Applications;    Machine Vision;   

DOI  :  10.5565/rev/elcvia.1337

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown