Automatic Extraction of Road Network Based on the Integration of Sentinel-1 and Sentinel-2 Satellite Images with Texture Analysis Features in Non-urban Space (Case Study: Shahroud-Miami Route)

Document Type : Research Paper


1 Mousavi*, S. M., MSc. Student of Road and Transportation, Faculty of Civil Engineering, Shahroud University of Technology, Shahroud, I. R. Iran.

2 Assistant Professor, Department of Road and Transportation, Faculty of Civil Engineering, Shahroud University of Technology, Shahroud, I. R. Iran.

3 Assistant Professor, Department of Geotechnic, Road and Surveying, Faculty of Civil Engineering, Shahroud University of Technology, Shahroud, I. R. Iran.


Remote sensing has wide applications in many scientific and research fields, including road engineering and transportation, the most important of which is extraction of road network and preparation of a schematic map of road network. Extraction of road network from satellite images is a complementary technology for accessing information, which simplifies the interpretation and analysis of image and improves the quality, which is one of the most important goals of planners. The main purpose of this study was to automatically extract road network on Shahroud-Miami route so that the resulting road network map can be used as the input of pavement management system (PMS). The proposed method in this study is based on the technique of merging and combining images of Sentinel-1 and Sentinel-2 satellites with the majority voting method in order to make maximum use of spectral and spatial information of multiple images (detail increase) instead of single image using texture features. Then, for the monitored classification, three classifications of artificial neural network (ANN), support vector machine (SVM) and maximum likelihood of similarity (ML) were used in two general classes of road and non-road. Random and homogeneous experimental samples and evaluation samples from the existing images and maps of the region were used to assess the accuracy of classification. Results of this study showed that integration of the results of classifications with the majority voting method improved the accuracy by 4% for Sentinel-1 satellite and 6% for Sentinel-2 satellite in identifying the route and the road network. Also, the kappa coefficient in the majority voting method has increased by about 0.11 for Sentinel-1 satellite and by about 0.06, compared to ANN (the best effective classification performance).


Abdelfattah, R. and Chokmani, K. 2017. “A semi automatic off-roads and trails extraction method from Sentinel-1 data”. International Geoscience and Remote Sensing Symposium (IGARSS), July, pp. 3728–3731.
Bakhtiari, H. R., Abdollahi, A. and Rezaeian, H. 2017. “Semi automatic road extraction from digital images”. Egypt. J. Remote Sens. Sp. Sci., 20(1): 117-123.
European Space Agency. 2020. “Sentinel Online- ESA”. Earth Online.
Gao, X., Sun, X., Zhang, Y., Yan, M., Xu, G., Sun, H., Jiao, J. and Fu, K. 2018. “An end-to-end neural network for road extraction from remote sensing imagery by multiple feature pyramid network”. IEEE Access, 6: 39401-39414.
Huang, C., Davis, L. S. and Townshend, J. R. G. 2002. “An assessment of support vector machines for land cover classification”. Int. J. Remote Sens., 23: 725-749.
Karathanassi, V., Kolokousis, P. and Ioannidou, S. 2007. “A comparison study on fusion methods using evaluation indicators”. Int. J. Remote Sens., 28(10): 2309-2341.
Kuncheva. L. I., Whitaker, C. J., Shipp, C. A. and Duin, R. P. W. 2003. “Limits on the majority vote accuracy in classifier fusion”. Pattern Anal. Appl., 6(1): 22-31.
Lazecky, M., Comut, C., Qin, Y. and Perissin, D. 2017. “Sentinel-1 interferometry system in the high-performance computing environment”. PP. 131-139. In; The Rise of Big Spatial Data, Springer.
Lu, D., Li, G., Moran, E., Dutra, L. and Batistella, M. 2014. “The roles of textural images in improving land-cover classification in the Brazilian Amazon”. Int. J. Remote Sens., 35(24): 8188-8207.
Lyons, M. B., Keith, D. A., Phinn, S. R., Mason, T. J. and Elith, J. 2018. “A comparison of resampling methods for remote sensing classification and accuracy assessment”. Remote Sens. Environ., 208: 145-153.
Mani, V. R. S. 2020. “A survey of multi sensor satellite image fusion techniques”. Int. J. Sens. Sens. Netw., 8(1): 1-10.
Mhangara, P., Mapurisa, W. and Mudau, N. 2020. “Comparison of image fusion techniques using Satellite pour l’Observation de la terre (SPOT) 6 satellite imagery”. Appl. Sci., 10(5): 1881.
Miao, Z., Shi, W., Zhang, H. and Wang, X. 2013. “Road centerline extraction from high-resolution imagery based on shape features and multivariate adaptive regression splines”. IEEE Geosci. Remote Sens. Lett., 10(3): 583-587.
Römer, H., Willroth, P., Kaiser, G., Vafeidis, A. T., Ludwig, R., Sterr, H. and Revilla Diez, J. 2012. “Potential of remote sensing techniques for tsunami hazard and vulnerability analysis–a case study from Phangnga province, Thailand”. Nat. Hazard Earth Sys. Sci., 12(6): 2103-2126.
Ruta, D. and Gabrys, B. 2000. “An overview of classifier fusion methods”. Comp. Inform. Sys., 7(1): 1-10.
Schölkopf, B. and Smola, A. 2005. “Support vector machines and kernel algorithms”. Encyclopedia of Biostatistics, Wiley, pp. 5328-5335.
Saghafi, M., Ahmadi, A. and Bigdeli, B. 2021. “Sentinel-1 and Sentinel-2 data fusion system for surface water extraction”. J. Appl. Remote Sens., 15(1): 014521.
Şatır, O. and Berberoğlu, S. 2012. “Land use/cover classification techniques using optical remotely sensed data in landscape planning”. Landscape Planning, InTech, Shanghai, China, pp. 21-55.‏
Shi, W., Miao, Z., Wang, Q. and Zhang, H. 2014. “Spectral-spatial classification and shape features for urban road centerline extraction”. IEEE Geosci. Remote Sens. Lett., 11(4): 788-792.
Sun, J., Yang, J., Zhang, C., Yun, W. and Qu, J. 2013. “Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method”. Math. Comp. Modell., 58(3-4): 573-581.
Wang, Z., Ziou, D., Armenakis, C., Li, D. and Li, Q. 2005. “A comparative analysis of image fusion methods”. IEEE Trans. Geosci. Remote Sens., 43(6): 1391-1402.
Wang, J. L., Qian, J. H. and Ma, R. B. 2013. “Urban road information extraction from high resolution remotely sensed image based on semantic model”. In: 21th International Conference on Geoinformatics, Shanghai.
Wang, J., Qin, Q., Yang, X., Wang, J., Ye, X. and Qin, X. 2014. “Automated road extraction from multi-resolution images using spectral information and texture”. International Geoscience and Remote Sensing Symposium (IGARSS), pp. 533-536.
Zhang, Q., Kong, Q., Zhang, C., You, S., Wei, H., Sun, R. and Li, L. 2019. “A new road extraction method using Sentinel-1 SAR images based on the deep fully convolutional neural network”. Eur. J. Remote Sens., 52(1): 572-582.