Advertisement

Mind the Gap: Modeling Local and Global Context in (Road) Networks

  • Javier A. Montoya-ZegarraEmail author
  • Jan D. Wegner
  • Ľubor Ladický
  • Konrad Schindler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

Abstract

We propose a method to label roads in aerial images and extract a topologically correct road network. Three factors make road extraction difficult: (i) high intra-class variability due to clutter like cars, markings, shadows on the roads; (ii) low inter-class variability, because some non-road structures are made of similar materials; and (iii) most importantly, a complex structural prior: roads form a connected network of thin segments, with slowly changing width and curvature, often bordered by buildings, etc. We model this rich, but complicated contextual information at two levels. Locally, the context and layout of roads is learned implicitly, by including multi-scale appearance information from a large neighborhood in the per-pixel classifier. Globally, the network structure is enforced explicitly: we first detect promising stretches of road via shortest-path search on the per-pixel evidence, and then select pixels on an optimal subset of these paths by energy minimization in a CRF, where each putative path forms a higher-order clique. The model outperforms several baselines on two challenging data sets, both in terms of precision/recall and w.r.t. topological correctness.

Keywords

Road Network Road Segment Road Width Reversible Jump Markov Chain Monte Carlo Topological Correctness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bajcsy, R., Tavakoli, M.: Computer recognition of roads from satellite pictures. IEEE Trans. Syst. Man Cybern. 6(9), 623–637 (1976)CrossRefGoogle Scholar
  2. 2.
    Bas, E., Erdogmus, D.: Principal curves as skeletons of tubular objects. Neuroinformatics 9, 181–191 (2011)CrossRefGoogle Scholar
  3. 3.
    Benmansour, F., Cohen, L.D.: Tubular structure segmentation based on minimal path method and anisotropic enhancement. IJCV 92, 192–210 (2011)CrossRefGoogle Scholar
  4. 4.
    Chai, D., Förstner, W., Lafarge, F.: Recovering line-networks in Images by junction-point processes. In: CVPR (2013)Google Scholar
  5. 5.
    Deschamps, T., Cohen, L.D.: Fast extraction of minimal paths in 3d images and applications to virtual endoscopy. Med. Image Anal. 5(4), 281–299 (2001)CrossRefGoogle Scholar
  6. 6.
    Doucette, P., Agouris, P., Stefanidis, A.: Automated road extraction from high resolution multispectral imagery. Photogram. Eng. Remote Sens. 70(12), 1405–1416 (2004)CrossRefGoogle Scholar
  7. 7.
    Fischler, M., Tenenbaum, J., Wolf, H.: Detection of roads and linear structures in low-resolution aerial imagery using a multisource knowledge integration technique. Comput. Graph. Image Process. 15, 201–223 (1981)CrossRefGoogle Scholar
  8. 8.
    van Gemert, J.C., Geusebroek, J.-M., Veenman, C.J., Smeulders, A.W.M.: Kernel codebooks for scene categorization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 696–709. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Grote, A., Heipke, C., Rottensteiner, F.: Road network extraction in suburban areas. Photogram. Rec. 27(137), 8–28 (2012)CrossRefGoogle Scholar
  10. 10.
    Heipke, C., Mayer, H., Wiedemann, C.: Evaluation of automatic road extraction. In: 3D Reconstruction and Modeling of Topographic Objects (1997)Google Scholar
  11. 11.
    Hinz, S., Baumgartner, A.: Automatic extraction of urban road networks from multi-view aerial imagery. ISPRS J. Photogram. Remote Sens. 58, 83–98 (2003)CrossRefGoogle Scholar
  12. 12.
    Hu, J., Razdan, A., Femiani, J.C., Cui, M., Wonka, P.: Road network extraction and intersection detection from aerial images by tracking road footprints. IEEE TGRS 45(12), 4144–4157 (2007)Google Scholar
  13. 13.
    Hussain, S., Triggs, B.: Visual recognition using local quantized patterns. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 716–729. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Kohli, P., Ladicky, L., Torr, P.H.S.: Robust higher order potentials for enforcing label consistency. In: CVPR (2008)Google Scholar
  15. 15.
    Kohli, P., Ladicky, L., Torr, P.H.S.: Robust higher order potentials for enforcing label consistency. IJCV 82(3), 302–324 (2009)CrossRefGoogle Scholar
  16. 16.
    Lacoste, C., Descombes, X., Zerubia, J.: Point processes for unsupervised line network extraction in remote sensing. PAMI 27(10), 1568–1579 (2005)CrossRefGoogle Scholar
  17. 17.
    Ladicky, L., Russell, C., Kohli, P., Torr, P.H.S.: Associative hierarchical CRFs for object class image segmentation. In: ICCV (2009)Google Scholar
  18. 18.
    Lafarge, F., Gimel’farb, G., Descombes, X.: Geometric feature extraction by a multimarked point process. PAMI 32(9), 1597–1609 (2010)CrossRefGoogle Scholar
  19. 19.
    Laptev, I., Mayer, H., Lindeberg, T., Eckstein, W., Steger, C., Baumgartner, A.: Automatic extraction of roads from aerial images based on scale space and snakes. MVA 12, 23–31 (2000)Google Scholar
  20. 20.
    Li, H., Yezzi, A.: Vessels as 4-D curves: global minimal 4-D paths to extract 3-D tubular surfaces and centerlines. IEEE TMI 26(9), 1213–1223 (2007)Google Scholar
  21. 21.
    Lindeberg, T.: Scale-space theory: A basic tool for analysing structures at different scales. J. Appl. Stat. 21(2), 224–270 (1994)Google Scholar
  22. 22.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  23. 23.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. Int. J. Comput. Vis. 43(1), 7–27 (2001)zbMATHCrossRefGoogle Scholar
  24. 24.
    Mayer, H., Hinz, S., Bacher, U., Baltsavias, E.: A test of automatic road extraction approaches. IAPRS 36(3), 209–214 (2006)Google Scholar
  25. 25.
    Mena, J., Malpica, J.: An automatic method for road extraction in rural and semi-urban areas starting from high resolution satellite imagery. Pattern Recogn. Lett. 26, 1201–1220 (2005)CrossRefGoogle Scholar
  26. 26.
    Miao, Z., Shi, W., Zhang, H., Wang, X.: Road centerline extraction from high-resolution imagery based on shape features and multivariate adaptive regression splines. IEEE GRSL 10(3), 583–587 (2013)Google Scholar
  27. 27.
    Mnih, V., Hinton, G.E.: Learning to detect roads in high-resolution aerial images. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 210–223. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  28. 28.
    Poullis, C., You, S.: Delineation and geometric modeling of road networks. ISPRS J. Photogram. Remote Sens. 65, 165–181 (2010)CrossRefGoogle Scholar
  29. 29.
    Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  30. 30.
    Shotton, J., Winn, J.M., Rother, C., Criminisi, A.: TextonBoost: joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  31. 31.
    Stoica, R., Descombes, X., Zerubia, J.: A Gibbs point process for road extraction from remotely sensed images. IJCV 57(2), 121–136 (2004)CrossRefGoogle Scholar
  32. 32.
    Türetken, E., Benmansour, F., Andres, B., Pfister, H., Fua, P.: Reconstructing loopy curvilinear structures using integer programming. In: CVPR (2013)Google Scholar
  33. 33.
    Türetken, E., Benmansour, F., Fua, P.: Automated reconstruction of tree structures using path classifiers and mixed integer programming. In: CVPR (2012)Google Scholar
  34. 34.
    Türetken, E., González, G., Blum, C., Fua, P.: Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors. Neuroinformatics 9, 279–302 (2011)CrossRefGoogle Scholar
  35. 35.
    Ünsalan, C., Sirmacek, B.: Road network detection using probabilistic and graph theoretical methods. IEEE TGRS 50(11), 4441–4453 (2012)Google Scholar
  36. 36.
    Wegner, J.D., Montoya-Zegarra, J.A., Schindler, K.: A higher-order CRF model for road network extraction. In: CVPR (2013)Google Scholar
  37. 37.
    Wiedemann, C., Heipke, C., Mayer, H., Jamet, O.: Empirical evaluation of automatically extracted road axes. In: CVPR Workshops (1998)Google Scholar
  38. 38.
    Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: CVPR (2005)Google Scholar
  39. 39.
    Zhao, T., Xie, J., Amat, F., Clack, N., Ahammad, P., Peng, H., Long, F., Myers, E.: Automated reconstruction of neuronal morphology based on local geometrical and global structural models. Neuroinformatics 9, 247–261 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Javier A. Montoya-Zegarra
    • 1
    Email author
  • Jan D. Wegner
    • 1
  • Ľubor Ladický
    • 2
  • Konrad Schindler
    • 1
  1. 1.Photogrammetry and Remote SensingETH ZürichZürichSwitzerland
  2. 2.Computer Vision GroupETH ZürichZürichSwitzerland

Personalised recommendations