Statistical evaluation and analysis of road extraction methodologies using a unique dataset from remote sensing
Authors
Cardim, Guilherme Pina; Da Silva, Erivaldo Antonio; Dias, Mauricio Araújo; Bravo Muñoz, Ignacio; Gardel Vicente, AlfredoIdentifiers
Permanent link (URI): http://hdl.handle.net/10017/60004DOI: 10.3390/rs10040620
ISSN: 2072-4292
Publisher
MDPI
Date
2018-04-13Bibliographic citation
Cardim, G.P. [et al.], 2018, "Statistical evaluation and analysis of road extraction methodologies using a unique dataset from remote sensing", Remote sensing, vol. 10, no. 4, art. no. 620, pp. 1-17.
Keywords
Road network extraction
Remote sensing images
Methodologies review
Image dataset
Evaluation metrics
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.3390/rs10040620Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2018 The authors
Access rights
info:eu-repo/semantics/openAccess
Abstract
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.
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