Abstract
In this work, an integrated framework comprising of pixel-based classification, road network filtering, and multi-scale Gabor filter is proposed to address the various prevailing issues in road centerline extraction from VHR images. The proposed framework is composed of three steps; generation of the initial road map, road network filtering and road centerline extraction. In the first step, pixel-based classification using support vector machine (SVM) is performed on VHR imagery to classify it into the road and non-road classes. In the road network filtering step, to retain the road features in classified imagery, an edge-preserving guided filter is applied and to improve the veracity of road extraction, undesirable components are eliminated by shape feature analysis. Finally, the complete and accurate road centerline are obtained by combining multi-scale Gabor filter and fast parallel thinning algorithm. The maximum response from a multi-scale Gabor filter is not only used for extraction but also used for reconstructing the broken road network, which is due to occlusions and artifacts. The proposed framework is implemented on publically available VHR images dataset. The simulation outcomes reflect the dominance of the proposed framework on different quantitative evaluation parameters as compared to up-to-date methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdollahi, A., Bakhtiari, H. R. R., & Nejad, M. P. (2018). Investigation of SVM and level set interactive methods for road extraction from google earth images. Journal of the Indian Society of Remote Sensing., 46(3), 423–430. https://doi.org/10.1007/s12524-017-0702-x
Amini, J. (2009). Road extraction from satellite images using a fuzzy-snake model. Cartographic Journal. Taylor & Francis, 46(2), 164–172. https://doi.org/10.1179/000870409X459923
Anil, P. N., & Natarajan, S. (2013). Road extraction using topological derivative and mathematical morphology. Journal of the Indian Society of Remote Sensing., 41(3), 719–724. https://doi.org/10.1007/s12524-012-0231-6
Cheng, G., et al. (2016). Accurate urban road centerline extraction from VHR imagery via multiscale segmentation and tensor voting. Neurocomputing., 205, 407–420. https://doi.org/10.1016/j.neucom.2016.04.026
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273–297.
Daugman, J. G. (1985). Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. JOSA A. Optical Society of America, 2(7), 1160–1169.
Guo, B., et al. (2011). Application of a fast linear feature detector to road extraction from remotely sensed imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing., 4(3), 626–631. https://doi.org/10.1109/JSTARS.2010.2094181
He, K., Sun, J., & Tang, X. (2013). Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence., 35(6), 1397–1409. https://doi.org/10.1109/TPAMI.2012.213
Heipke, C. et al (1997). Evaluation of automatic road extraction. International Archives of Photogrammetry and Remote Sensing. 10.1.1.30.7686.
Huang, X., & Zhang, L. (2009). Road centreline extraction from high-resolution imagery based on multiscale structural features and support vector machines. International Journal of Remote Sensing., 30(8), 1977–1987. https://doi.org/10.1080/01431160802546837
Jiang, W., Lam, K., & Shen, T. (2009). Efficient Edge Detection Using Simplified Gabor Wavelets. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)., 39(4), 1036–1047. https://doi.org/10.1109/TSMCB.2008.2011646
Kang, X., Li, S., & Benediktsson, J. A. (2013). Spectral–spatial hyperspectral image classification with edge-preserving filtering. IEEE transactions on geoscience and remote sensing., 52(5), 2666–2677.
Kestur, R., et al. (2018). UFCN: a fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle. Journal of Applied Remote Sensing., 12(01), 1. https://doi.org/10.1117/1.jrs.12.016020
Liu, D., & Xia, F. (2010). Assessing object-based classification: Advantages and limitations. Remote Sensing Letters. Taylor & Francis, 1(4), 187–194. https://doi.org/10.1080/01431161003743173
Liu, R., et al. (2016). Improved road centerlines extraction in high-resolution remote sensing images using shear transform, directional morphological filtering and enhanced broken lines connection. Journal of Visual Communication and Image Representation., 40, 300–311. https://doi.org/10.1016/j.jvcir.2016.06.024
Liu, R., et al. (2020). A semi-supervised high-level feature selection framework for road centerline extraction. IEEE Geoscience and Remote Sensing Letters., 17(5), 894–898. https://doi.org/10.1109/LGRS.2019.2931928
Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing., 28(5), 823–870. https://doi.org/10.1080/01431160600746456
Mena, J. B. (2003). State of the art on automatic road extraction for GIS update: A novel classification. Pattern Recognition Letters., 24(16), 3037–3058. https://doi.org/10.1016/S0167-8655(03)00164-8
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics., 9(1), 62–66.
Poullis, C., & You, S. (2010). Delineation and geometric modeling of road networks. ISPRS Journal of Photogrammetry and Remote Sensing., 65(2), 165–181. https://doi.org/10.1016/j.isprsjprs.2009.10.004
Sang, N. et al. (2004) Multiscale Centerline Extraction of Angiogram Vessels Using Gabor Filters. In: Zhang J., He JH., Fu Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_89
Shi, W., et al. (2014a). Spectral-spatial classification and shape features for urban road centerline extraction. IEEE Geoscience and Remote Sensing Letters., 11(4), 788–792. https://doi.org/10.1109/LGRS.2013.2279034
Shi, W., Miao, Z., & Debayle, J. (2014b). An integrated method for urban main-road centerline extraction from optical remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3359–3372. https://doi.org/10.1109/TGRS.2013.2272593
Singh, P. P., & Garg, R. D. (2013). Automatic road extraction from high resolution satellite image using adaptive global thresholding and morphological operations. Journal of the Indian Society of Remote Sensing, 41(3), 631–640. https://doi.org/10.1007/s12524-012-0241-4
Singh, P. P., & Garg, R. D. (2014a). A two-stage framework for road extraction from high-resolution satellite images by using prominent features of impervious surfaces. International Journal of Remote Sensing., 35(24), 8074–8107. https://doi.org/10.1080/01431161.2014.978956
Singh, P. P., & Garg, R. D. (2014b). Classification of high resolution satellite images using equivariant robust independent component analysis. Smart Innovation, Systems and Technologies., 1, 283–290. https://doi.org/10.1007/978-3-319-07353-8_34
Sironi, A., Lepetit, V. and Fua, P. (2014) ‘Multiscale centerline detection by learning a scale-space distance transform’, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2697–2704. doi: https://doi.org/10.1109/CVPR.2014.351
Song, M., & Civco, D. (2004). Road extraction using SVM and image segmentation. Photogrammetric Engineering and Remote Sensing., 70(12), 1365–1371. https://doi.org/10.14358/PERS.70.12.1365
Sujatha, C., & Selvathi, D. (2015). Connected component-based technique for automatic extraction of road centerline in high resolution satellite images. Eurasip Journal on Image and Video Processing. https://doi.org/10.1186/s13640-015-0062-9
Türetken, E., Benmansour, F. and Fua, P. (2012) ‘Automated reconstruction of tree structures using path classifiers and mixed integer programming, in 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 566–573.
Unsalan, C., & Sirmacek, B. (2012). Road network detection using probabilistic and graph theoretical methods. IEEE Transactions on Geoscience and Remote Sensing., 50(11), 4441–4453.
Vplab (no date) Vplab. Available at: http://www.cse.iitm.ac.in/~vplab/satellite.html Date Accessed 15–07–2020.
Wang, J., et al. (2016). ‘A new approach to urban road extraction using high-resolution aerial image. ISPRS International Journal of Geo-Information., 5(7), 114. https://doi.org/10.3390/ijgi5070114
Wang, W., et al. (2016). Sciencedirect a review of road extraction from remote sensing images. Journal of Traffic and Transportation Engineering (English Edition)., 3(3), 271–282. https://doi.org/10.1016/j.jtte.2016.05.005
Zelang, et al. (2013). Road centerline extraction from high-resolution imagery based on shape features and multivariate adaptive regression splines. IEEE Geoscience and Remote Sensing Letters, 10(3), 583–587. https://doi.org/10.1109/LGRS.2012.2214761
Zhang, T. Y., & Suen, C. Y. (1984). ‘A fast parallel algorithm for thinning digital patterns’, Communications of the ACM. ACM New York, NY, USA, 27(3), 236–239.
Zhang, Z., Liu, Q., & Wang, Y. (2018). Road extraction by deep residual U-Net. IEEE Geoscience and Remote Sensing Letters., 15(5), 749–753. https://doi.org/10.1109/LGRS.2018.2802944
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Soni, P.K., Rajpal, N. & Mehta, R. Road Centerline Extraction From VHR Images Using SVM and Multi-Scale Maximum Response Filter. J Indian Soc Remote Sens 49, 1519–1532 (2021). https://doi.org/10.1007/s12524-021-01329-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-021-01329-2