Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling
Morocho-Cayamcela, Manuel Eugenio (Escuela Superior Politécnica del Litoral)

Date:	2021
Abstract:	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.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Computer vision ; Scene understanding ; Pattern recognition ; Separation and segmentation ; Applications ; Machine vision ; Other applications ; Image analysis and processing
Published in:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 19 Núm. 2 (2020) , p. 17-21 (Special Issue on Recent PhD Thesis Dissemination (2020)) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v19-n2-Morocho-cayamcela
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/378893
DOI: 10.5565/rev/elcvia.1337

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