Linear feature extraction using perceptual grouping and graph-cuts

ABSTRACT

In this paper we present a novel system for the detection and extraction of road map information from high-resolution satellite imagery.

Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (gabor filtering, tensor voting) and segmentation (graph-cuts) into a unified framework to address the problems of road feature detection and classification. Local orientation information is derived using a bank of gabor filters and is refined using tensor voting. A segmentation method based on global optimization by graph-cuts is developed for segmenting foreground(road pixels) and background objects while preserving oriented boundaries. Road centerlines are detected using pairs of gaussian-based filters and road network vector maps are finally extracted using a tracking algorithm.

The proposed system works with a single or multiple images, and any available elevation information. User interaction is limited and is performed at the begining of the system execution. User intervention is allowed at any stage of the process to refine or edit the automatically generated results.

REFERENCES

• 1: A. Baumgartner, C. T. Steger, H. Mayer, W. Eckstein, and H. Ebner. Automatic road extraction in rural areas. In ISPRS Congress, pages 107--112, 1999.
• 2: Y. Boykov and M.-P. Jolly. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In ICCV, pages 105--112, 2001.
• 3: Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. In ICCV, pages 377--384, 1999.
• 4: S. Clode, F. Rottensteiner, and P. Kootsookos. Improving city model determination by using road detection from lidar data. In IAPRSSIS Vol. XXXVI - 3/W24, pp. 159--164, Vienna, Austria, 2005.
• 5: Marian S. Bartlett, A Comparison of Gabor Filter Methods for Automatic Detection of Facial Landmarks, Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, p.242, May 20-21, 2002
• 6: P. Hofmann. Detecting buildings and roads from ikonos data using additional elevation information. In Dipl.-Geogr.
• 7: I. Laptev , H. Mayer , T. Lindeberg , W. Eckstein , C. Steger , A. Baumgartner, Automatic extraction of roads from aerial images based on scale space and snakes, Machine Vision and Applications, v.12 n.1, p.23-31, July 2000 [doi>10.1007/s001380050004]
• 8: G. Lisini, C. Tison, D. Cherifi, F. Tupin, and P. Gamba. Improving road network extraction in high-resolution sar images by data fusion. In CEOS SAR Workshop 2004, 2004.
• 9: A. Massad, M. Babós, and B. Mertsching. Perceptual grouping in grey-level images by combination of gabor filtering and tensor voting. In ICPR (2), pages 677--680, 2002.
• 10: Gerard Medioni , Mi-Suen Lee , Chi-Keung Tang, Computational Framework for Segmentation and Grouping, Elsevier Science Inc., New York, NY, 2000
• 11: B. Wessel. Road network extraction from sar imagery supported by context information. In ISPRS Proceedings, 2004.
• 12: C. Zhang, E. Baltsavias, and A. Gruen. Knowledge-based image analysis for 3d road construction. In AJG 1(4), 2001.
• 13: Q. Zhang and I. Couloigner. Automated road network extraction from high resolution multi-spectral imagery. In ASPRS Proceedings, 2006.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications

General Terms:
Algorithms, Design

Keywords:
feature extraction, global optimization, graph-cuts, perceptual grouping, road network