Abstract
In recent years, the neural network architecture has developed rapidly and has been widely used in the semantic segmentation of remote sensing images. In this paper, we apply the neural network to the road network extraction of high-resolution remote sensing images. Subsequently, a series of single-pixel coordinate points are obtained by using the refinement method. Given the different lengths of road sections, our algorithm uses a double-loop mechanism to perform multi-scale fitting of them, which improves the rough results of the neural network and enhances the accuracy of road network extraction. For multiple line segments that may be on the same road, we also propose appropriate rules to classify and merge them. Our experimental results show that compared with other methods of road network extraction, our approach can obtain better results.
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References
Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica Int. J. Geogr. Inf. Geovisualization 10(2), 112−122
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381−395 (1981)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hilditch, C.J.: Linear skeleton from square cupboards. Mach. Intell. 6, 403–420 (1969)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Miao, Z., Shi, W., Samat, A., Lisini, G., Gamba, P.: Information fusion for urban road extraction from VHR optical satellite images. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 9(5), 1817–1829 (2016)
Ramer, U.: An iterative procedure for the polygonal approximation of plane curves. Comput. Graph. Image Process. 1(3), 244–256 (1972)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI, Part III. Lecture Notes in Computer Science, vol. 9351, pp. 234–241. Springer (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Soni, P.K., Rajpal, N., Mehta, R.: A comparison of road network extraction from high resolution images. In: Proceedings of the First International Conference on Secure Cyber Computing and Communication (ICSCCC), pp. 525–531. IEEE (2018)
Zhang, T., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27(3), 236–239 (1984)
Acknowledgments
This work was supported in part by the Scientific & Technological Innovation 2030 - “New Generation AI” Key Project (No. 2021ZD0114001; No. 2021ZD0114000), and the Science and Technology Commission of Shanghai Municipality (No. 21511102200). Zhihui Wang is the corresponding author of this work.
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Wang, Z., Wang, Y., Ni, Y. (2024). An Approach for Extracting Road Network from Remote Sensing Images. In: Huang, DS., Zhang, C., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14868. Springer, Singapore. https://doi.org/10.1007/978-981-97-5600-1_31
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DOI: https://doi.org/10.1007/978-981-97-5600-1_31
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