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

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Pattern Recognition (GCPR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8753))

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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.

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Notes

  1. 1.

    As often done in aerial imaging, when it is available we regard the height-field from dense matching as an additional image channel, and do not separately refer to it.

  2. 2.

    In principle the two-stage classification could potentially be replaced by some form of structured prediction. This would require significantly more training data.

  3. 3.

    If desired the \(P^N\)-Potts model would also allow for conventional pairwise potentials. We did not find them necessary, the context-based unaries are already locally smooth.

  4. 4.

    Graz was kindly provided by Microsoft Photogrammetry. Vaihingen is part of the ISPRS benchmark http://www.itc.nl/ISPRS_WGIII4/tests_datasets.html.

  5. 5.

    \(\kappa \) avoids biases due to uneven class distribution. E.g., for an image with 10 % road pixels a result without a single road pixel has 90 % overall accuracy, but \(\kappa \)=0 %.

References

  1. Bajcsy, R., Tavakoli, M.: Computer recognition of roads from satellite pictures. IEEE Trans. Syst. Man Cybern. 6(9), 623–637 (1976)

    Article  Google Scholar 

  2. Bas, E., Erdogmus, D.: Principal curves as skeletons of tubular objects. Neuroinformatics 9, 181–191 (2011)

    Article  Google Scholar 

  3. Benmansour, F., Cohen, L.D.: Tubular structure segmentation based on minimal path method and anisotropic enhancement. IJCV 92, 192–210 (2011)

    Article  Google Scholar 

  4. Chai, D., Förstner, W., Lafarge, F.: Recovering line-networks in Images by junction-point processes. In: CVPR (2013)

    Google Scholar 

  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)

    Article  Google Scholar 

  6. Doucette, P., Agouris, P., Stefanidis, A.: Automated road extraction from high resolution multispectral imagery. Photogram. Eng. Remote Sens. 70(12), 1405–1416 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  9. Grote, A., Heipke, C., Rottensteiner, F.: Road network extraction in suburban areas. Photogram. Rec. 27(137), 8–28 (2012)

    Article  Google Scholar 

  10. Heipke, C., Mayer, H., Wiedemann, C.: Evaluation of automatic road extraction. In: 3D Reconstruction and Modeling of Topographic Objects (1997)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  14. Kohli, P., Ladicky, L., Torr, P.H.S.: Robust higher order potentials for enforcing label consistency. In: CVPR (2008)

    Google Scholar 

  15. Kohli, P., Ladicky, L., Torr, P.H.S.: Robust higher order potentials for enforcing label consistency. IJCV 82(3), 302–324 (2009)

    Article  Google Scholar 

  16. Lacoste, C., Descombes, X., Zerubia, J.: Point processes for unsupervised line network extraction in remote sensing. PAMI 27(10), 1568–1579 (2005)

    Article  Google Scholar 

  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. Lafarge, F., Gimel’farb, G., Descombes, X.: Geometric feature extraction by a multimarked point process. PAMI 32(9), 1597–1609 (2010)

    Article  Google Scholar 

  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. 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. 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. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  28. Poullis, C., You, S.: Delineation and geometric modeling of road networks. ISPRS J. Photogram. Remote Sens. 65, 165–181 (2010)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  31. Stoica, R., Descombes, X., Zerubia, J.: A Gibbs point process for road extraction from remotely sensed images. IJCV 57(2), 121–136 (2004)

    Article  Google Scholar 

  32. Türetken, E., Benmansour, F., Andres, B., Pfister, H., Fua, P.: Reconstructing loopy curvilinear structures using integer programming. In: CVPR (2013)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  35. Ünsalan, C., Sirmacek, B.: Road network detection using probabilistic and graph theoretical methods. IEEE TGRS 50(11), 4441–4453 (2012)

    Google Scholar 

  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. Wiedemann, C., Heipke, C., Mayer, H., Jamet, O.: Empirical evaluation of automatically extracted road axes. In: CVPR Workshops (1998)

    Google Scholar 

  38. Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: CVPR (2005)

    Google Scholar 

  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)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland

    Javier A. Montoya-Zegarra, Jan D. Wegner & Konrad Schindler

  2. Computer Vision Group, ETH Zürich, Zürich, Switzerland

    Ľubor Ladický

Authors
  1. Javier A. Montoya-Zegarra
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  2. Jan D. Wegner
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  3. Ľubor Ladický
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  4. Konrad Schindler
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Corresponding author

Correspondence to Javier A. Montoya-Zegarra .

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Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Münster, Germany

    Xiaoyi Jiang

  2. Computer Science Department 5, University of Erlangen-Nürnberg, Erlangen, Germany

    Joachim Hornegger

  3. Department of Computer Science, University of Kiel, Kiel, Germany

    Reinhard Koch

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Montoya-Zegarra, J.A., Wegner, J.D., Ladický, Ľ., Schindler, K. (2014). Mind the Gap: Modeling Local and Global Context in (Road) Networks. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_17

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  • DOI: https://doi.org/10.1007/978-3-319-11752-2_17

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