The commercial high-resolution imaging satellite with 1 m spatial resolution
IKONOS is an important data source of information for urban planning and geographical
information system (GIS) applications. In this paper, a morphological method is
proposed. The proposed method combines the automatic thresholding and morphological
operation techniques to extract the road centerline of the urban environment. This
method intends to solve urban road centerline problems, vehicle, vegetation, building
etc. Based on this morphological method, an object extractor is designed to extract
road networks from highly remote sensing images. Some filters are applied in this
experiment such as line reconstruction and region filling techniques to connect
the disconnected road segments and remove the small redundant. Finally, the thinning
algorithm is used to extract the road centerline. Experiments have been conducted
on a high-resolution IKONOS and QuickBird images showing the efficiency of the proposed
method.
References
[1]
Baltsavias, E., Gruen, A. and van Gool, L. (2001) Automatic Extraction of Man-Made Objects from Aerial and Space Images (III). Balkema, Lisse, The Netherlands.
[2]
de Castro, F.S.P. and Centeno, J.A.S. (2010) Road Extraction from ALOS Images Using Mathematical Morphology. IAPRS Journal, 38, Part 7B.
[3]
Sirmacek, B. and Unsalan, C. (2010) Road Network Extraction Using Edge Detection and Spatial Voting. International Conference on Pattern Recognition, Istanbul, 23-26 August 2010, 3113-3116. http://dx.doi.org/10.1109/icpr.2010.762
[4]
Yang, J. and Wang, R. (2007) Classified Road Detection from Satellite Images Based on Perceptual Organization. International Journal of Remote Sensing, 28, 4653-4669. http://dx.doi.org/10.1080/01431160701250382
[5]
Li, L.-W., Liu, J.-P. and Yin, Z.-W. (2005) Road Extraction from High Resolution Remote Sensing Image Based on Mathematic Morphology. Remote Sensing Information, 5, 9-11.
[6]
Daryal, N. and Kumar, V. (2010) Linear Extraction of Satellite Imageries Using Mathematical Morphology. International Journal of Computer Applications, 3. http://dx.doi.org/10.5120/717-1009
[7]
Baumgartner, A., Steger, C., Mayer, H. and Eckstein, W. (1997) Multi-Resolution, Semantic Objects, and Context for Road Extraction. In Semantic Modeling for the Acquisition of Topographic Information from Images and Maps, Birkhauser Verlag, 140-156.
[8]
Unsalan, C. and Boyer, K. (2005) A System to Detect Houses and Residential Street Networks in Multispectral Satellite Images. Computer Vision and Image Understanding, 98, 432-461. http://dx.doi.org/10.1016/j.cviu.2004.10.006
[9]
Kim, Y., Seo, B. and Oh, J. (2000) The Effect of the Resolution of Satellite Images on the Interpretability and Detectability of Geographic Information. Proceedings of Pecora 14-Land Satellite Information in the Next Decade, III Conference, Denver Colorado, 6-9 December 2000.
[10]
Coulter, L., Stow, D., Kiracofe, B., Langevin, C., Chen, D., Daeschner, S., Service, D. and Kaiser, J. (1999) Deriving Current Land-Use Information for Metropolitan Transportation Planning through Integration of Remotely Sensed Data and GIS. Photogrammetric Engineering and Remote Sensing, 65, 1293-1300.
[11]
Fraser, C., Baltsvias, E. and Gruen, A. (2002) Processing of IKONOS Imagery for Sub-Meter 3D Positioning and Building Extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 56, 177-194.
http://dx.doi.org/10.1016/S0924-2716(02)00045-X
[12]
Jin, H. and Feng, Y. (2010) Towards an Automatic Road Lane Marks Extraction Based on Isodata Segmentation and Shadow Detection from Large-Scale Aerial Images.
[13]
Doucette, P., Agouris, P. and Stefanidis, A. (2004) Automated Road Extraction from High Resolution Multispectral Imagery. Photogrammetric Engineering & Remote Sensing, 70, 1405-1416.
http://dx.doi.org/10.14358/PERS.70.12.1405
[14]
Jin, X. and Davis, C.H. (2005) An Integrated System for Automatic Road Mapping from High-Resolution Multi-Spectral Satellite Imagery by Information Fusion. Information Fusion, 6, 257-273.
http://dx.doi.org/10.1016/j.inffus.2004.06.003
[15]
Valero, S., et al. (2010) Advanced Directional Mathematical Morphology for the Detection of the Road Network in Very High Resolution Remote Sensing Images. Pattern Recognition Letters, 31, 1120-1127.
http://dx.doi.org/10.1016/j.patrec.2009.12.018
[16]
Goodman, J. (1997) Global Thresholding and Multiple-Pass Parsing. Proceedings of the 2nd Conference on Empirical Methods in Natural Language Processing, Providence, 1-2 August 1997, 11-25.
[17]
Singh, P.P. and Garg, R.D. (2013) Automatic Road Extraction from High Resolution Satellite Image Using Adaptive Global Thresholding and Morphological Operations. Journal of the Indian Society of Remote Sensing, 41, 631-640.
http://dx.doi.org/10.1007/s12524-012-0241-4
[18]
Li, L.-W., Liu, J.-P. and Yin, Z.-W. (2005) Road Extraction from High Resolution Remote Sensing Image Based on Mathematic Morphology. Remote Sensing Information, 5, 9-11.
[19]
Zhang, T.Y. and Suen, C.Y. (1984) A Fats Parallel Algorithm for Thinning Digital 568 Patterns. Communications of the ACM, 27, 236-239. http://dx.doi.org/10.1145/357994.358023
[20]
Benediktsson, J.A. (2003) Classification and Feature Extraction for Remote Sensing Images from Urban Areas Based on Morphological Transformations. IEEE Transactions on Geoscience and Remote Sensing, 41, 1940-1949.
http://dx.doi.org/10.1109/TGRS.2003.814625
[21]
Ma, H., Qin, Q., Du, S., Wang, L. and Jin, C. (2007) Road Extraction from ETM Panchromatic Image Based on Dualedge Following. IEEE International Geoscience and Remote Sensing Symposium, Barcelona, 23-28 July 2007, 460-463.
[22]
Bacher, U. and Mayer, H. (2005) Automatic Road Extraction from Multispectral High Resolution Satellite Images. Object Extraction for 3D City Models, Road Databases and Traffic Monitoring, 36, 29-34.