سابِق|تال

Development and prospect of road extraction method for optical remote sensing image

  • role： First author第一作者
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

  • Email：daijg03@163.com
  • Introduction： 戴激光，1978年生,男,副教授，研究方向为遥感理论和应用。E-mail: daijg03@163.com
DAI Jiguang1,  
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

WANG Yang1,  
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

DU Yang1,  
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

ZHU Tingting1,  
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

XIE Shizhe1,  
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

LI Chengcheng1,  
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

FANG Xinxin1

ملخص

Road extraction from remote sensing image has important practical value. It presents a major issue in the field of remote sensing. In recent years, the geometric texture of the target objects of aviation and satellite optical images has been refined in the wake of the rapid development of aviation technology. Thus, this technology provides a sufficient basis for automatic extraction of road information. However, full automation of road extraction through existing methods is difficult. In view of this issue, this study collects and synthesizes the existing methods on the basis of a large number of related literature in recent years. Ultimately, these road extraction methods are divided into four categories: template matching, knowledge-driven, object-oriented, and deep learning.Template matching methods can be generally divided into rule template and variable template according to template type. The difference between the two types is whether the template can be drawn with regular graphics. Template matching mainly consists of the following three steps: template design, measure analysis, and location update. The template design can usually be set manually or by certain rules. Then, the target template is given in the measure analysis. Furthermore, the extreme value of the region is found by the measure function within the defined area. Lastly, the location of the road centerline is dynamically updated.Roads, as artificial ruled features, provide large information. Hence, knowledge-driven methods based on relevant knowledge are used in road extraction work. On the basis of the relationship between knowledge and road, this study divides the knowledge-driven methods by three kinds: geometric knowledge, context knowledge, and auxiliary knowledge. Three methods are described as follows: (1) Geometric knowledge. The model is mainly constructed on the basis of the geometric features of the road. (2) Contextual knowledge. The method utilizes road-related auxiliary knowledge (motor vehicles, trees, zebra crossings, et al.) to assist in identifying the road. (3) Assisting knowledge. Road extraction is guided by using multisource remote sensing data, vector data, GPS data, navigation data, and public source data.With the rapid increase of spatial resolution of optical remote sensing images, object-oriented methods have gradually become one of key methods in road extraction. First, the method selects the spectral and geometric feature criteria to segment image. Second, the geometric radiation similarity criterionis used to classify the segmentation region. Finally, the road extraction results are released by post-processing methods, such as mathematical morphology, matching tracking, and tensor voting.Road extraction research belongs to the problem of remote sensing image interpretation, whereas deep learning supplies a new opportunity for satellite image interpretation in a semantic direction. Deep learning extracts road information with high precision through convolution, pooling, and training. Nevertheless, road breakage and mistaken extraction problems still exist.At the end of the study, the direction of development of road extraction is envisioned. Two main trends are proposed: (1) a multimethod complementary road extraction system and (2) deep integration of deep learning and traditional methods. Thus, the detection area should be studied further.

مفهوم

optic;remote sensing imagery;road extraction;template matching;knowledge-driven;object-oriented;deep learning

References

  1. 1.
    Alshehhi R and Marpu P R . 2017. Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 245-260
  2. 2.
    Alshehhi R, Marpu P R, Lee Woon W L and Mura M D . 2017. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 139-149
  3. 3.
    Ameri F and Valadan Zoej M J . 2015. Road vectorisation from high-resolution imagery based on dynamic clustering using particle swarm optimisation. Photogrammetric Record, 30(152): 363-386
  4. 4.
    Amini J, Lucas C, Saradjian M, Azizi A and Sadeghian S . 2002. Fuzzy logic system for road identification using IKONOS images. The Photogrammetric Record, 17(99): 493-503
  5. 5.
    Auclair-Fortier M F, Ziou D, Armenakis C and Wang S . 1999. Survey of Work on Road Extraction in Aerial and Satellite Images. University of Sherbrooke: 6-11
  6. 6.
    Baatz M and Schäpe A . 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation//Proceedings of Angewandte Geographische Informationsverarbeitung XII. Heidelberg: Wichmann: 12-23
  7. 7.
    Babaali K O, Zigh E, Djebbouri M and Kadiri M . 2014. Survey of some new road extraction methods. The International Journal of Engineering and Science (IJES), 3(11): 28-33
  8. 8.
    Bajcsy R and Tavakoli M . 1976. Computer recognition of roads from satellite pictures. IEEE Transactions on Systems, Man, and Cybernetics, SMC- 6(9): 623-637
  9. 9.
    Baumgartner A, Steger C, Mayer H, Eckstein W and Ebner H . 1999. Automatic road extraction based on multi-scale, grouping, and context. Photogrammetric Engineering and Remote Sensing, 65(7): 777-785.
  10. 10.
    Blaschke T, Hay G J, Kelly M, Lang S, Hofmann P, Addink E, Queiroz Feitosa R, van der Meer F, van der Werff H, van Coillie F and Tiede D . 2014. Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 180-191
  11. 11.
    Blaschke T, Hay G J, Weng Q H and Resch B , 2011. Collective sensing: integrating geospatial technologies to understand urban systems—an overview. Remote Sensing, 3(8): 1743-1776
  12. 12.
    Brust C A, Sickert S, Simon M, Rodner E and Denzler J . 2015. Convolutional patch networks with spatial prior for road detection and urban scene understanding. international conference on computer vision theory and applications, Available:https://arxiv.org/abs/1502.06344
  13. 13.
    Cao C Q and Sun Y . 2014. Automatic road centerline extraction from imagery using road GPS data. Remote Sensing, 6(9): 9014-9033
  14. 14.
    Cao F Z, Xu Y B, Zhu B S and Li R S . 2015. Semi-automatic road centerline extraction from high-resolution remote sensing by image utilizing dynamic programming. Journal of Geomatics Science and Technology, 32(6): 615-618, 625
  15. 15.
    Cao F Z, Zhu S L, Zhu B S, Li R S and Meng W C . 2016. Tracking road centerlines from remotely sensed imagery using mean shift and Kalman filtering. Acta Geodaetica et Cartographica Sinica, 45(2): 205-212, 223
  16. 16.
    Cardim G P, da Silva E A, Dias M A, Bravo I and Gardel A . 2018. Statistical evaluation and analysis of road extraction methodologies using a unique dataset from remote sensing. Remote Sensing, 10(4): 620
  17. 17.
    Chen H, Yin L L and Ma L . 2014. Research on road information extraction from high resolution imagery based on global precedence//Proceedings of the 2014 3rd International Workshop on Earth Observation and Remote Sensing Applications. Changsha, China: IEEE: 151-155
  18. 18.
    Chen Z X, Ren J Q, Tang H J, Shi Y, Leng P, Liu J, Wang L M, Wu W B, Yao Y M and Hasiyuya . 2016. Progress and perspectives on agricultural remote sensing research and applications in China. Journal of Remote Sensing, 20(5): 748-767
  19. 19.
    Cheng G and Han J W . 2016. A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117: 11-28
  20. 20.
    Cheng G L, Wang Y, Xu S B, Wang H Z, Xiang S M, and Pan C H . 2017. Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 55(6): 3322-3337
  21. 21.
    Cheng M J and Weng Q H . 2017. Urban road extraction from combined data sets of high-resolution satellite imagery and LiDAR data using GEOBIA//Quattrochi D A, ed. Integrating Scale in Remote Sensing and GIS. New York: CRC Press: 283-302
  22. 22.
    Cho N G, Yuille A and Lee S W . 2018. A novel linelet-based representation for line segment detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(5): 1195-1208
  23. 23.
    Cortes C and Vapnik V . 1995. Support-vector networks. Machine Learning, 20(3): 273-297
  24. 24.
    Coulibaly I, Spiric N, Lepage R and St-Jacques M . 2018. Semiautomatic road extraction from VHR images based on multiscale and spectral angle in case of earthquake. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(1): 238-248
  25. 25.
    Dai J G, Du Y, Fang X X, Wang Y and Miao Z P . 2018. Road extraction method for high resolution optical remote sensing images with multiple feature constraints. Journal of Remote Sensing, 22(5): 777-791
  26. 26.
    Dai J G, Zhang L, Zhu E Z, Liao J C, Fang X X and Li J W . 2017. Principal line detection in remote sensing image. Journal of Remote Sensing, 21(2): 228-238
  27. 27.
    Ding L, Zhang B M, Guo H T and Lu J . 2017. Automatic road extraction from high-resolution remote sensing images assisted by vector data. Journal of Remote Sensing, 21(1): 84-95
  28. 28.
    Fischler M A and Elschlager R A . 1973. The representation and matching of pictorial structures. IEEE Transactions on Computers, C- 22(1): 67-92
  29. 29.
    Frizzelle B G, Evenson K R, Rodriguez D A and Laraia B A . 2009. The importance of accurate road data for spatial applications in public health: customizing a road network. International Journal of Health Geographics, 8: 24
  30. 30.
    Fu G . 2014. Road Extraction Method Using Multi-Source Remote Sensing Data. Beijing: Tsinghua University: 98-111
  31. 31.
    Fu G, Zhao H R, Li C and Shi L M . 2013. Road detection from optical remote sensing imagery using circular projection matching and tracking strategy. Journal of the Indian Society of Remote Sensing, 41(4): 819-831
  32. 32.
    Gong J Y and Ji S P . 2018. Photogrammetry and deep learning. Acta Geodaetica et Cartographica Sinica, 47(6): 693-704
  33. 33.
    Grinias I, Panagiotakis C and Tziritas G . 2016. MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 122: 145-166
  34. 34.
    Gruen A and Li H H . 1997. Semi-automatic linear feature extraction by dynamic programming and LSB-Snakes. Photogrammetric Engineering and Remote Sensing, 63(8): 985-995
  35. 35.
    Han J, Guo Q and Li A . 2017. Road extraction based on unsupervised classification and geometric-texture-spectral features for high-resolution remote sensing images. Journal of Image and Graphics, 22(12): 1788-1797
  36. 36.
    Haverkamp D S . 2002. Extracting straight road structure in urban environments using IKONOS satellite imagery. Optical Engineering, 41(9): 2107-2111
  37. 37.
    Heijmans H, Buckley M and Talbot H . 2005. Path openings and closings. Journal of Mathematical Imaging and Vision, 22( 2/3): 107-119
  38. 38.
    Herumurti D, Uchimura K, Koutaki G and Uemura T . 2013. Urban road network extraction based on zebra crossing detection from a very high resolution RGB aerial image and DSM data//Proceedings of 2013 International Conference on Signal-Image Technology and Internet-Based Systems. Kyoto Japan: IEEE: 79-84
  39. 39.
    Hu X Y, Tao C V and Hu Y . 2004. Automatic road extraction from dense urban area by integrated processing of high resolution imagery and LIDAR data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 35 (B 3): 288-292
  40. 40.
    Hu X Y, Zhang Z X and Zhang J Q . 2000. An approach of semiautomated road extraction from aerial images based on template matching and neural network. International Archives of Photogrammetry and Remote Sensing, 33 (B 3): 994-999
  41. 41.
    Hu X Y, Zhang Z X and Zhang J Q . 2002. Semiautomatic extraction of linear object form aerial image. Journal of Image and Graphics, 7(2): 137-140
  42. 42.
    Hu Z W, Li Q Q, Zou Q, Zhang Q and Wu G F . 2016. A bilevel scale-sets model for hierarchical representation of large remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54(12): 7366-7377
  43. 43.
    Huang X and Zhang L P . 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
  44. 44.
    Huang X and Zhang L P . 2013. An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 51(1): 257-272
  45. 45.
    Huang X, Zhang L P and Li P X . 2007. Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 4(2): 260-264
  46. 46.
    Inglada J . 2007. Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features. ISPRS Journal of Photogrammetry and Remote Sensing, 62(3): 236-248
  47. 47.
    Karaman E, Çinar U, Gedik E, Yardımcı Y and Halıcı U . 2012. A new algorithm for automatic road network extraction in multispectral satellite images//Proceedings of the 4th GEOBIA. Rio de Janeiro, Brazil: [s.n.]:455-459
  48. 48.
    Kass M, Witkin A and Terzopoulos D . 1988. Snakes: active contour models. International Journal of Computer Vision, 1(4): 321-331
  49. 49.
    Kim T, Park S R, Kim M G, Jeong S and Kim K O . 2004. Tracking road centerlines from high resolution remote sensing images by least squares correlation matching. Photogrammetric Engineering and Remote Sensing, 70(12): 1417-1422
  50. 50.
    Krizhevsky A, Sutskever I and Hinton G E . 2012. ImageNet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: Curran Associates Inc.: 1097-1105
  51. 51.
    Kumar M, Singh R K, Raju P L N and Krishnamurthy Y V N . 2014. Road network extraction from high resolution multispectral satellite imagery based on object oriented techniques. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II- 8: 107-110
  52. 52.
    Lécun Y, Bottou L, Bengio Y and Haffner P . 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278-2324
  53. 53.
    Leninisha S and Vani K . 2015. Water flow based geometric active deformable model for road network. ISPRS Journal of Photogrammetry and Remote Sensing, 102: 140-147
  54. 54.
    Li C and Li F F . 2013. Auto-extracting sub-pixel line feature of digital images based on hypothesis testing. Acta Geodaetica et Cartographica Sinica, 42(1): 67-72
  55. 55.
    Li C F, Liu L, Zhou T G and Zhang L . 2009. Study on road extraction from high-resolution remote sensing image based on street trees. Remote Sensing Information, (1): 32-35
  56. 56.
    Li C M, Kao C Y, Gore J C and Ding Z H . 2008. Minimization of region-scalable fitting energy for image segmentation. IEEE Transactions on Image Processing, 17(10): 1940-1949
  57. 57.
    Li G, An J L and Chen C H . 2011. Automatic road extraction from high-resolution remote sensing image based on bat model and mutual information matching. Journal of Computers, 6(11): 2417-2426
  58. 58.
    Li H S, Huang P P and Su Y . 2015. A method for road extraction from remote sensing imagery. Remote Sensing for Land and Resources, 27(2): 56-62
  59. 59.
    Li J, Qin Q M, Xie C and Zhao Y . 2012. Integrated use of spatial and semantic relationships for extracting road networks from floating car data. International Journal of Applied Earth Observation and Geoinformation, 19: 238-247
  60. 60.
    Li M M, Stein A, Bijker W and Zhan Q M . 2016a. Region-based urban road extraction from VHR satellite images using binary partition tree. International Journal of Applied Earth Observation and Geoinformation, 44: 217-225
  61. 61.
    Li P K, Zang Y, Wang C, Li J, Cheng M, Luo L and Yu Y . 2016b. Road network extraction via deep learning and line integral convolution//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China: IEEE: 1599-1602
  62. 62.
    Li Q P, Fan H C, Luan X C, Yang B S and Liu L . 2014. Polygon-based approach for extracting multilane roads from OpenStreetMap urban road networks. International Journal of Geographical Information Science, 28(11): 2200-2219
  63. 63.
    Lian R B, Wang W X and Li J . 2018. Road extraction from high-resolution remote sensing images based on adaptive circular template and saliency map. Acta Geodaetica et Cartographica Sinica, 47(7): 950-958
  64. 64.
    Lin X G, Zhang J X, Li H T and Yang J H . 2009. Semi-automatic extraction of ribbon road from high resolution remotely sensed imagery by a T-shaped template matching. Geomatics and Information Science of Wuhan University, 34(3): 293-296
  65. 65.
    Liu B, Wu H Y, Wang Y D and Liu W M . 2015. Main road extraction from ZY-3 grayscale imagery based on directional mathematical morphology and VGI prior knowledge in urban areas. PLoS One, 10(9): e0138071
  66. 66.
    Liu R Y, Song J F, Quan Y N, Xu P F, Xue Q, Yang Y and Miao Q G . 2017. Automatic road extraction method for high-resolution remote sensing images. Journal of Xidian University, 44(1): 100-105
  67. 67.
    Liu W F, Zhang Z Q, Li S Y and Tao D P . 2017. Road detection by using a generalized Hough transform. Remote Sensing, 9(6): 590
  68. 68.
    Lv Z, Jia Y H, Zhang Q and Chen Y F . 2017. An adaptive multifeature sparsity-based model for semiautomatic road extraction from high-resolution satellite images in urban areas. IEEE Geoscience and Remote Sensing Letters, 14(8): 1238-1242
  69. 69.
    Maboudi M, Amini J, Hahn M and Saati M . 2016. Road network extraction from VHR satellite images using context aware object feature integration and tensor voting. Remote Sensing, 8(8): 637
  70. 70.
    Maboudi M, Amini J, Hahn M and Saati M . 2017. Object-based road extraction from satellite images using ant colony optimization. International Journal of Remote Sensing, 38(1): 179-198
  71. 71.
    Maboudi M, Amini J, Malihi S and Hahn M . 2018 Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images. ISPRS Journal of Photogrammetry and Remote Sensing, 138: 151-163
  72. 72.
    Maggiori E, Manterola H L and del Fresno M . 2015. Perceptual grouping by tensor voting: a comparative survey of recent approaches. IET Computer Vision, 9(2): 259-277
  73. 73.
    Mayer H, Laptev I, Baumgartner A and Steger C . 1997. Automatic road extraction based on multi-Scale modeling, context, and snakes. International Archives of Photogrammetry and Remote Sensing, 32(3): 106-113.
  74. 74.
    McKeown D M and Denlinger J L . 1988. Cooperative methods for road tracking in aerial imagery//Proceedings CVPR '88: the Computer Society Conference on Computer Vision and Pattern Recognition. Ann Arbor, MI, USA: IEEE: 662-672
  75. 75.
    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
  76. 76.
    Miao Z L, Shi W Z, Gamba P and Li Z B . 2015. An object-based method for road network extraction in VHR satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10): 4853-4862
  77. 77.
    Miao Z L, Shi W Z and Zhang H . 2013. A road centerline extraction algorithm from high resolution satellite imagery. Journal of China University of Mining and Technology, 42(5): 887-892, 898
  78. 78.
    Miao Z L, Wang B, Shi W Z and Zhang H . 2014. A semi-automatic method for road centerline extraction from VHR images. IEEE Geoscience and Remote Sensing Letters, 11(11): 1856-1860
  79. 79.
    Mnih V and Hinton G E . 2010. Learning to detect roads in high-resolution aerial images//Proceedings of the 11th European Conference on Computer Vision. Heraklion, Crete, Greece: Springer: 210-223
  80. 80.
    Movaghati S, Moghaddamjoo A and Tavakoli A . 2010. Road extraction from satellite images using particle filtering and extended Kalman filtering. IEEE Transactions on Geoscience and Remote Sensing, 48(7): 2807-2817
  81. 81.
    Nakaguro Y, Makhanov S S and Dailey M N . 2011. Numerical experiments with cooperating multiple quadratic snakes for road extraction. International Journal of Geographical Information Science, 25(5): 765-783
  82. 82.
    Nikfar M, Zoej M J V, Mokhtarzade M and Shoorehdeli M A . 2015. Designing a new framework using type-2 FLS and cooperative-competitive genetic algorithms for road detection from ikonos satellite imagery. Remote Sensing, 7(7): 8271-8299
  83. 83.
    Osher S and Sethian J A . 1988. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79(1): 12-49
  84. 84.
    Poullis C . 2014. Tensor-Cuts: a simultaneous multi-type feature extractor and classifier and its application to road extraction from satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 95: 93-108
  85. 85.
    Pudaruth S . 2016. Extraction of roads from remotely sensed images using a multi-angled template matching technique//Proceedings of the 3rd International Symposium on Computer Vision and the Internet. Jaipur, India: ACM: 21-29
  86. 86.
    Rahimi S, Arefi H and Bahmanyar R . 2015. Automatic road extraction based on integration of high resolution LiDAR and aerial imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W 5: 583-587
  87. 87.
    Raziq A, Xu A G and Li Y . 2016. Automatic extraction of urban road centerlines from high-resolution satellite imagery using automatic thresholding and morphological operation method. Journal of Geographic Information System, 8(4): 517-525
  88. 88.
    Rochery M, Jermyn I H and Zerubia J . 2006. Higher order active contours. International Journal of Computer Vision, 69(1): 27-42
  89. 89.
    Saba F, Valadan Zoej M J and Mokhtarzade M . 2016. Optimization of multiresolution segmentation for object-oriented road detection from high-resolution images. Canadian Journal of Remote Sensing, 42(2): 75-84
  90. 90.
    Schubert H, van de Gronde J J, Roerdink J B T M . 2016. Efficient computation of greyscale path openings. Mathematical Morphology - Theory and Applications, 1(1): 189-202
  91. 91.
    Sengupta S K, Lopez A S, Brase J M and Paglieroni D W . 2004. Phase-based road detection in multi-source images//Proceedings of 2004 IEEE International Geoscience and Remote Sensing Symposium. Anchorage, AK, USA: IEEE: 3833-3836
  92. 92.
    Sethian J A . 1996. A fast marching level set method for monotonically advancing fronts. Proceedings of the National Academy of Sciences of the United States of America, 93(4): 1591-1595
  93. 93.
    Sghaier M O and Lepage R . 2016. Road extraction from very high resolution remote sensing optical images based on texture analysis and beamlet transform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(5): 1946-1958
  94. 94.
    Shackelford A K and Davis C H . 2003. Fully automated road network extraction from high-resolution satellite multispectral imagery//Proceedings of 2003 IEEE International Geoscience and Remote Sensing Symposium. Toulouse, France: IEEE: 461-463
  95. 95.
    Shan J, Qin K, Huang C Q, Hu X Y, Yu Y, Hu Q W, Lin Z Y, Chen J P and Jia T . 2014. Methods of crowd sourcing geographic data processing and analysis. Geomatics and Information Science of Wuhan University, 39(4): 390-396
  96. 96.
    Shao Y Z, Guo B X, Hu X Y and Di L P . 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
  97. 97.
    Shi W Z, Zhu C Q and Wang Y . 2001. Road feature extraction from remotely sensed image: review and prospects. Acta Geodaetica et Cartographica Sinica, 30(3): 257-262
  98. 98.
    Simonyan K and Zisserman A . 2014. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10).
  99. 99.
    Song M J and Civco D . 2004. Road extraction using SVM and image segmentation. Photogrammetric Engineering and Remote Sensing, 70(12): 1365-1371
  100. 100.
    Sui H G, Hua L and Gong J Y . 2003. Automatic extraction of road networks from remotely sensed images based on GIS knowledge//Proceedings of SPIE 4898, Image Processing and Pattern Recognition in Remote Sensing. Hangzhou, China: SPIE: 226-238
  101. 101.
    Sun C Y, Zhou T G, Chen S B, Shen J W, Wang J F and Yang H . 2015. Research of a semi-automatic extraction method for linear features based on rectangular template matching. Journal of Southwest University (Natural Science), 37(7): 155-160
  102. 102.
    Talbot H and Appleton B . 2007. Efficient complete and incomplete path openings and closings. Image and Vision Computing, 25(4): 416-425
  103. 103.
    Tang W and Zhao S H . 2011 Road extraction in quaternion space from high spatial resolution remotely sensed images basing on GVF Snake model. Journal of Remote Sensing, 15(5): 1040-1052
  104. 104.
    Toth C and Jóźków G . 2016. Remote sensing platforms and sensors: a survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115: 22-36
  105. 105.
    Treash K and Amaratunga K . 2000. Automatic road detection in grayscale aerial images. Journal of Computing in Civil Engineering, 14(1): 60-69[DOI:
  106. 106.
    Tunde A M and Adeniyi E E . 2012. Impact of road transport on agricultural development: a Nigerian example. Ethiopian Journal of Environmental Studies and Management, 5(3): 232-238
  107. 107.
    Uemura T, Uchimura K and Koutaki G . 2011. Road extraction in urban areas using boundary code segmentation for DSM and aerial RGB images. Journal of the Institute of Image Electronics Engineers of Japan, 40(1): 74-85
  108. 108.
    Unsalan C and Sirmacek B . 2012. Road network detection using probabilistic and graph theoretical methods. IEEE Transactions on Geoscience and Remote Sensing, 50(11): 4441-4453
  109. 109.
    Vosselman G and de Knecht J . 1995. Road tracing by profile matching and Kaiman filtering//Gruen A, Kuebler O and Agouris P, eds. Automatic Extraction of Man-Made Objects from Aerial and Space Images. Basel: Birkhäuser Basel: 265-274
  110. 110.
    Wan J and Yilmaz A . 2018. Machine vision special issue: building match graph using deep convolution feature for structure from motion. Acta Geodaetica et Cartographica Sinica, 47(6): 882-891
  111. 111.
    Wang F P, Wang W X, Xue B Y, Cao T and Gao T . 2017. Road extraction from high-spatial-resolution remote sensing image by combining GVF Snake with salient features. Acta Geodaetica et Cartographica Sinica, 46(12): 1978-1985
  112. 112.
    Wang W F, Zhu S H, Feng Y H and Ding W L . 2012. Parallel edges detection from remote sensing image using local orientation coding. Acta Optica Sinica, 32(3): 0315001
  113. 113.
    Wang W X, Yang N, Zhang Y, Wang F P, Cao T and Eklund P . 2016. A review of road extraction from remote sensing images. Journal of Traffic and Transportation Engineering (English Edition), 3(3): 271-282
  114. 114.
    Wang Z H, Hu X Y and Shan J . 2015. A rasterization-based hierarchical approach for urban road centerline extraction from crowdsourcing GPS floating car data. Bulletin of Surveying and Mapping, (8): 22-24, 34
  115. 115.
    Wegner J D, Montoya-Zegarra J A and Schindler K . 2015. Road networks as collections of minimum cost paths. ISPRS Journal of Photogrammetry and Remote Sensing, 108: 128-137
  116. 116.
    Wei Y N, Wang Z L and Xu M . 2017. Road structure refined CNN for road extraction in aerial image. IEEE Geoscience and Remote Sensing Letters, 14(5): 709-713
  117. 117.
    Wiedemann, C , 2003. External evaluation of road networks. International Archives Of Photogrammetry, Remote Sensing And Spatial Information Sciences 34 (Part 3/W 8), 93-98
  118. 118.
    Willrich F . 2002. Quality control and updating of road data by GIS-driven road extraction from imagery. Proceedings of Joint International Symposium on Geospatial Theory, Processing, and Applications. Ottawa: [s.n.]: 1-7
  119. 119.
    Wu L and Hu Y A . 2010. A survey of automatic road extraction from remote sensing images. Acta Automatica Sinica, 36(7): 912-922
  120. 120.
    Wu X W and Xu H Q . 2010. Level set method major roads information extract from high-resolution remote-sensing imagery. Journal of Astronautics, 31(5): 1495-1502
  121. 121.
    Xu C Y and Prince J L . 1997. Gradient vector flow: a new external force for snakes//Proceedings of 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Juan, Puerto Rico, USA: IEEE: 66-71
  122. 122.
    Xu Y Y, Xie Z, Feng Y X and Chen Z L . 2018. Road extraction from high-resolution remote sensing imagery using deep learning. Remote Sensing, 10(9): 1461
  123. 123.
    Yang C, Duraiswami R, DeMenthon D and Davis L . 2003. Mean-shift analysis using quasi-newton methods//Proceedings of 2003 International Conference on Image Processing. Barcelona, Spain: IEEE: II- 447
  124. 124.
    Yang Y and Zhu C Q . 2010. Extracting road centrelines from high-resolution satellite images using active window line segment matching and improved SSDA. International Journal of Remote Sensing, 31(9): 2457-2469
  125. 125.
    Yin D D, Du S H, Wang S W and Guo Z . 2015. A direction-guided ant colony optimization method for extraction of urban road information from very-high-resolution images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10): 4785-4794
  126. 126.
    Yu J, Yu F, Zhang J and Liu Z Y . 2013. High resolution remote sensing image road extraction combining region growing and road-unit. Geomatics and Information Science of Wuhan University, 38(7): 761-764
  127. 127.
    Zhang J Q, Liu P F, Wang H and Liu Y L . 2010. Semi-automatic road extraction with Meanshift algorithm. Geomatics and Information Science of Wuhan University, 35(6): 719-722
  128. 128.
    Zhang J X, Lin X G, Liu Z J and Shen J . 2011. Semi-automatic road tracking by template matching and distance transformation in urban areas. International Journal of Remote Sensing, 32(23): 8331-8347
  129. 129.
    Zhang Q P and Couloigner I . 2006. Benefit of the angular texture signature for the separation of parking lots and roads on high resolution multi-spectral imagery. Pattern Recognition Letters, 27(9): 937-946
  130. 130.
    Zhang Z X, Liu Q J and Wang Y H . 2018. Road extraction by deep residual U-Net. IEEE Geoscience and Remote Sensing Letters, 15(5):749-753
  131. 131.
    Zhu E Z, Song W D and Dai J G . 2016. Road extraction of high-resolution remote sensing images based on improved SVM. Science of Surveying and Mapping, 41(12): 224-228

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website