ROAD NETWORK EXTRACTION METHODS FROM REMOTE SENSING IMAGES: A REVIEW PAPER

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Miral Patel
Ashish M. Kothari

Abstract




Remote Sensing images are consists of photographs of Earth or other planets captured by means of satellites, helicopter, rocket, drone etc.. The quality of remote sensing images depends on sensor, camera used to capture images and number of bands. Due to




 

 

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How to Cite
Patel, M., & Ashish M. Kothari. (2022). ROAD NETWORK EXTRACTION METHODS FROM REMOTE SENSING IMAGES: A REVIEW PAPER. International Journal of Next-Generation Computing, 13(2). https://doi.org/10.47164/ijngc.v13i2.376

References

  1. Abraham, L. and Sasikumar, M. 2013. A fuzzy based road network extraction from degraded DOI: https://doi.org/10.1109/ICACCI.2013.6637494
  2. satellite images. In 2013 International Conference on Advances in Computing, Communications
  3. and Informatics (ICACCI). IEEE, 2032–2036.
  4. Alshehhi, R., Marpu, P. R., Woon, W. L., and Dalla Mura, M. 2017. Simultaneous
  5. extraction of roads and buildings in remote sensing imagery with convolutional neural
  6. networks. ISPRS Journal of Photogrammetry and Remote Sensing 130, 139–149.
  7. Anil, P. and Natarajan, S. 2010. A novel approach using active contour model for semiautomatic
  8. road extraction from high resolution satellite imagery. In 2010 Second International
  9. Conference on Machine Learning and Computing. IEEE, 263–266.
  10. Bakhtiari, H. R. R., Abdollahi, A., and Rezaeian, H. 2017. Semi automatic road extraction
  11. from digital images. The Egyptian Journal of Remote Sensing and Space Science 20, 1,
  12. –123.
  13. Cheng, G., Wang, Y., Xu, S., Wang, H., Xiang, S., and Pan, C. 2017. Automatic road
  14. detection and centerline extraction via cascaded end-to-end convolutional neural network.
  15. IEEE Transactions on Geoscience and Remote Sensing 55, 6, 3322–3337.
  16. Farabet, C., Couprie, C., Najman, L., and LeCun, Y. 2012. Learning hierarchical features
  17. for scene labeling. IEEE transactions on pattern analysis and machine intelligence 35, 8,
  18. –1929.
  19. Ghasemloo, N., Mobasheri, M. R., Zare, A. M., and Eftekhari, M. M. 2013. Road and
  20. tunnel extraction from spot satellite images using neural networks.
  21. He, H., Yang, D., Wang, S., Wang, S., and Li, Y. 2019. Road extraction by using atrous
  22. spatial pyramid pooling integrated encoder-decoder network and structural similarity loss.
  23. Remote Sensing 11, 9, 1015.
  24. Hormese, J. and Saravanan, C. 2016. Automated road extraction from high resolution DOI: https://doi.org/10.1016/j.protcy.2016.05.180
  25. satellite images. Procedia Technology 24, 1460–1467.
  26. Lan, M., Zhang, Y., Zhang, L., and Du, B. 2020. Global context based automatic road
  27. segmentation via dilated convolutional neural network. Information Sciences 535, 156–171. DOI: https://doi.org/10.1016/j.ins.2020.05.062
  28. Lee, H. Y., Park, W., Lee, H.-K., and Kim, T.-g. 2000. Towards knowledge-based extraction
  29. of roads from 1 m-resolution satellite images. In 4th IEEE Southwest Symposium on Image
  30. Analysis and Interpretation. IEEE, 171–176.
  31. Liu, R., Miao, Q., Song, J., Quan, Y., Li, Y., Xu, P., and Dai, J. 2019. Multiscale road
  32. centerlines extraction from high-resolution aerial imagery. Neurocomputing 329, 384–396. DOI: https://doi.org/10.1016/j.neucom.2018.10.036
  33. Liu, Y., Yao, J., Lu, X., Xia, M., Wang, X., and Liu, Y. 2018. Roadnet: Learning
  34. to comprehensively analyze road networks in complex urban scenes from high-resolution
  35. remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing 57, 4,
  36. –2056.
  37. Long, J., Shelhamer, E., and Darrell, T. 2015. Fully convolutional networks for semantic DOI: https://doi.org/10.1109/CVPR.2015.7298965
  38. segmentation. In Proceedings of the IEEE conference on computer vision and pattern
  39. recognition. 3431–3440.
  40. Mangala, R. and Bhirud, S. 2011. Extraction of road network from high resolution satellite DOI: https://doi.org/10.1145/1980022.1980215
  41. images using ann. In Proceedings of the International Conference & Workshop on Emerging
  42. Trends in Technology. 899–906.
  43. ¨Ozkaya, M. 2012. Road extraction from high resolution satellite images. International Archives DOI: https://doi.org/10.5194/isprsarchives-XXXIX-B4-143-2012
  44. of the Photogrammetry, Remote Sensing and Spatial Information Sciences 39, B4.
  45. Ronneberger, O., Fischer, P., and Brox, T. 2015. U-net: Convolutional networks for
  46. biomedical image segmentation. In International Conference on Medical image computing
  47. and computer-assisted intervention. Springer, 234–241.
  48. Senthilnath, J., Omkar, S., Mani, V., Tejovanth, N., Diwakar, P., and Shenoy, A. B.
  49. Hierarchical clustering algorithm for land cover mapping using satellite images. IEEE
  50. journal of selected topics in applied earth observations and remote sensing 5, 3, 762–768. DOI: https://doi.org/10.1109/JSTARS.2012.2201549
  51. Shen, J., Lin, X., Shi, Y., and Wong, C. 2008. Knowledge-based road extraction from high
  52. resolution remotely sensed imagery. In 2008 Congress on Image and Signal Processing.
  53. Vol. 4. IEEE, 608–612.
  54. Shi, Q., Liu, X., and Li, X. 2017. Road detection from remote sensing images by generative DOI: https://doi.org/10.1109/ACCESS.2017.2773142
  55. adversarial networks. IEEE access 6, 25486–25494.
  56. Song, M. and Civco, D. 2004. Road extraction using svm and image segmentation. Photogrammetric DOI: https://doi.org/10.14358/PERS.70.12.1365
  57. Engineering & Remote Sensing 70, 12, 1365–1371.
  58. Soni, P. K., Rajpal, N., and Mehta, R. 2020. Semiautomatic road extraction framework
  59. based on shape features and ls-svm from high-resolution images. Journal of the Indian
  60. Society of Remote Sensing 48, 3, 513–524. DOI: https://doi.org/10.1007/s12524-019-01077-4
  61. Vincent, L. and Soille, P. 1991. Watersheds in digital spaces: an efficient algorithm based DOI: https://doi.org/10.1109/34.87344
  62. on immersion simulations. IEEE Transactions on Pattern Analysis & Machine Intelligence
  63. , 06, 583–598.
  64. Wang, D. 1997. A multiscale gradient algorithm for image segmentation using watershelds. DOI: https://doi.org/10.1016/S0031-3203(97)00015-0
  65. Pattern recognition 30, 12, 2043–2052.
  66. Wang, S., Yang, H., Wu, Q., Zheng, Z., Wu, Y., and Li, J. 2020. An improved methodfor road extraction from high-resolution remote-sensing images that enhances boundary DOI: https://doi.org/10.3390/s20072064
  67. information. Sensors 20, 7, 2064.
  68. Wang, W., Yang, N., Zhang, Y., Wang, F., Cao, T., and Eklund, P. 2016. A review of
  69. road extraction from remote sensing images. Journal of traffic and transportation engineering
  70. (english edition) 3, 3, 271–282.
  71. Wei, Y., Zhang, K., and Ji, S. 2020. Simultaneous road surface and centerline extraction
  72. from large-scale remote sensing images using cnn-based segmentation and tracing. IEEE
  73. Transactions on Geoscience and Remote Sensing 58, 12, 8919–8931.
  74. Xin, J., Zhang, X., Zhang, Z., and Fang, W. 2019. Road extraction of high-resolution remote
  75. sensing images derived from denseunet. Remote Sensing 11, 21, 2499.
  76. Xu, G., Zhang, D., and Liu, X. 2009. Road extraction in high resolution images from google DOI: https://doi.org/10.1109/ICICS.2009.5397470
  77. earth. In 2009 7th International Conference on Information, Communications and Signal
  78. Processing (ICICS). IEEE, 1–5.
  79. Yager, N. and Sowmya, A. 2003. Support vector machines for road extraction from remotely DOI: https://doi.org/10.1007/978-3-540-45179-2_36
  80. sensed images. In International Conference on Computer Analysis of Images and Patterns.
  81. Springer, 285–292.
  82. Yang, X., Li, X., Ye, Y., Lau, R. Y., Zhang, X., and Huang, X. 2019. Road detection
  83. and centerline extraction via deep recurrent convolutional neural network u-net. IEEE
  84. Transactions on Geoscience and Remote Sensing 57, 9, 7209–7220.
  85. Zhang, Q., Kong, Q., Zhang, C., You, S., Wei, H., Sun, R., and Li, L. 2019. A new road
  86. extraction method using sentinel-1 sar images based on the deep fully convolutional neural
  87. network. European Journal of Remote Sensing 52, 1, 572–582.
  88. Zhang, Z., Liu, Q., and Wang, Y. 2018. Road extraction by deep residual u-net. IEEE DOI: https://doi.org/10.1109/IJCNN.2019.8851728
  89. Geoscience and Remote Sensing Letters 15, 5, 749–753.
  90. Zhou, L., Zhang, C., and Wu, M. 2018. D-linknet: Linknet with pretrained encoder and
  91. dilated convolution for high resolution satellite imagery road extraction. In Proceedings of
  92. the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 182–186.