Skip to main content
SpringerLink
Log in

Semi Automatic Road Extraction by Fusion of High Resolution Optical and Radar Images

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Two new methods for fusion of high-resolution optical and radar satellite images have been proposed to extract roads in high quality in this paper. Two fusion methods, including neural network and knowledge-based fusion are introduced. The first proposed method consists of two stages: (i) separate road detection using each dataset and (ii) fusion of the results obtained using a neural network. In this method, the neural networks are separately applied on high-resolution IKONOS and TerraSAR-X images for road detection, using a variety of texture parameters. The outputs of two neural networks, as well as the spectral features of optical image, are used in a third neural network as inputs. The second method is a knowledge-based fusion using thresholds of narrow roads and vegetation gray levels. First roads are extracted from each source separately. The outputs are then compared and advantages and disadvantages of each data source are investigated . The results obtained from accuracy assessment show the efficiency of the proposed methods. Furthermore, the comparison of the results showed the superiority of the first algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  • Al-Ali, M. (2011). Assessment of high resolution SAR imagery for mapping flood plain water bodies: a comparison between Radarsat-2 and TerraSAR-X, Ph D thesis, Department of Geography Durham University.

  • Dekker, R. J. (2008). Road extraction and network building from synthetic aperture,radar images using apriori information, TNO Defence, Security and Safety, PO Box 96864, 2509 JG The Hague, The Netherlands.

  • Haralick, R., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610–621.

    Article  Google Scholar 

  • Hedman, K., Wessel, B., & Stilla, U. (2005). A fusion strategy for extracted road networks from multi aspect SAR images. Photogrammetry and remote sensing, Technische Universitaet Muenchen, Arcisstrasse 21, 80333 Muenchen, Germany.

  • Mena, J. B. (2003). “State of the art on automatic road extraction for GIS update: a novel classification “ pattern recognition letters, 24 3037–58.

  • Mohammadzadeh, A. M. (2008). Automatic main road extraction from high resolution satellite imageries by means of self-learning fuzzy-GA algorithm. Journal of Applied Sciences, 8(19), 3431–3438.

    Article  Google Scholar 

  • Mohammadzadeh, A. M. (2010). A self organizing fuzzy segmentation method for road detection from high resolution satellite images. PE & RS, 76(1), 27–35.

    Google Scholar 

  • Mohammadzadeh, A. M. (2009). Automatic main road extraction from high resolution satellite imageries by means of particle swarm optimization applied to a fuzzy based mean calculation approach. IJSRS, 37, 173–184.

  • Mohammadzadeh, A., Tavakoli, A., & Valadan Zoej, M. J. (2006). Road extraction based on fuzzy logic and mathematical morphology from Pan-sharpened IKONOS images. The Photogrammetric Record, 21(113), 44–60.

    Article  Google Scholar 

  • Mokhtarzade, M. (2008). Integration of textural and spectral information in neural networks for automatic road extraction from high resolution satellite images, Ph D thesis, Department ofGeomatic KNToosi University of technology.

  • Mokhtarzade, M., & Valadan Zoej, M. J. (2007). Road detection from high resolution satellite images using artificial neural networks. International Journal of Applied Earth Observation and Geoinformation, 9(1), 32–40.

    Article  Google Scholar 

  • Tupin, F., Maitre, H., Mangin, J. F., Nicolas, J. M., & Pechersky, E. (1998). Detection of linear features in SAR images: application to road network extraction. IEEE Transactions on Geoscience and Remote Sensing, 36(2), 434–453.

    Article  Google Scholar 

  • Wessel, B. (2004). Road network extraction from Sar imagery supported by context information Photogrammetry and remote sensing, Technische universitaet Muenchen,80290 Muenchen, Germany, isprs.org.

Download references

Author information

Authors and Affiliations

  1. Department of Remote Sensing and Photogrammetry, K. N. Toosi University of Technology, Tehran, P.O. Box 15875-4416, Iran

    Elahe Khesali, Mohammad Javad Valadan Zoej & Mehdi Mokhtarzade

  2. Civil and Environmental Engineering Department, School of Engineering, Shiraz University, Shiraz, Iran

    Maryam Dehghani

Authors
  1. Elahe Khesali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Javad Valadan Zoej
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mehdi Mokhtarzade
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maryam Dehghani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elahe Khesali.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khesali, E., Zoej, M.J.V., Mokhtarzade, M. et al. Semi Automatic Road Extraction by Fusion of High Resolution Optical and Radar Images. J Indian Soc Remote Sens 44, 21–29 (2016). https://doi.org/10.1007/s12524-015-0480-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-015-0480-2

Keywords

Search

Navigation