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Open AccessArticle

Statistical Evaluation and Analysis of Road Extraction Methodologies Using a Unique Dataset from Remote Sensing

1
School of Sciences and Technology, São Paulo State University (UNESP), Presidente Prudente 19060-900, Brazil
2
Politechnic School, University of Alcalá (UAH), 28805 Alcalá de Henares, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(4), 620; https://doi.org/10.3390/rs10040620
Received: 6 March 2018 / Revised: 10 April 2018 / Accepted: 13 April 2018 / Published: 18 April 2018
(This article belongs to the Section Remote Sensing Image Processing)
In the scientific literature, multiple studies address the application of road extraction methodologies to a particular cartographic dataset. However, it is difficult for any study to perform a more reliable comparison among road extraction methodologies when their results come from different cartographic datasets. Therefore, aiming to enable a more reliable comparison among different road extraction methodologies from the scientific literature, this study proposed a statistical evaluation and analysis of road extraction methodologies using a common image dataset. To achieve this goal, we setup a dataset containing remote sensing images of three different road types, highways, cities network and rural paths, and a group of images from the ISPRS (International Society for Photogrammetry and Remote Sensing) dataset. Furthermore, three road extraction methodologies were selected from the literature, in accordance with their availability, to be processed and evaluated using well-known statistical metrics. The achieved results are encouraging and indicate that the proposed statistical evaluation and analysis can allow researchers to evaluate and compare road extraction methodologies using this common dataset extracting similar characteristics to obtain a more reliable comparison among them. View Full-Text
Keywords: road network extraction; remote sensing images; methodologies review; image dataset; evaluation metrics road network extraction; remote sensing images; methodologies review; image dataset; evaluation metrics
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MDPI and ACS Style

Cardim, G.P.; Silva, E.A.; Dias, M.A.; Bravo, I.; Gardel, A. Statistical Evaluation and Analysis of Road Extraction Methodologies Using a Unique Dataset from Remote Sensing. Remote Sens. 2018, 10, 620.

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