Skip to main content
SpringerLink
Log in

Methods for Texture-Based Classification of Urban Fringe Areas from Medium and High Resolution Satellite Imagery

  • Chapter
  • First Online:
Spatial Inequalities

Part of the book series: GeoJournal Library ((GEJL,volume 110))

  • 1316 Accesses

Abstract

The spatial expansion of Accra’s residential areas has been remarkable during the last decade. Following the implementation of liberalization policies from 1983 to the present, many legal obstacles to investments in the housing sector have gradually been removed. The peri-urban development is dominated by residential areas of low density with one- or two-story houses. Most areas are in need of services and infrastructure. Visually these new urban areas are characterized by a high percentage of plots covered with natural vegetation or exposed surfaces in between finished or partly finished buildings. This chapter discusses methods for satellite-based classification of peri-urban neighborhoods in order to support a more efficient planning process for service and infrastructure provision. More specifically, the chapter focuses on methods for mapping the continuum of urban development levels found within the fringe areas of Accra. The goal is to produce maps that depict the spatial properties of this gradual rural-to-urban transition zone more accurately than those produced using traditional image classification strategies.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Barnsley, M. J., Moller-Jensen, L., & Barr, S. (2001). Inferring urban land use by spatial and structural pattern recognition. In J.-P. Donnay, M. Barnsley, & P. Longley (Eds.), Remote sensing and urban analysis (pp. 115–144). London: Taylor & Francis.

    Chapter  Google Scholar 

  • Baud, I., Kuffer, M., Pfeffer, K., Sliuzas, R., & Karuppannan, S. (2010). Understanding heterogeneity in metropolitan India: The added value of remote sensing for analyzing sub-standard residential areas. International Journal of Applied Earth Observation and Geoinformation, 12, 359–374.

    Article  Google Scholar 

  • Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 2–16.

    Article  Google Scholar 

  • Gough, K. V. (1999). The changing role of urban governance in Accra, Ghana. Third World Planning Review, 21(4), 397–414.

    Google Scholar 

  • Grant, R. (2007). Geographies of investment: How do the wealthy build new houses in Accra, Ghana? Urban Forum, 18(1), 31–59.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Herold, M., Liu, X., & Clarke, K. C. (2003). Spatial metrics and image texture for mapping urban land use. Photogrammetric Engineering and Remote Sensing, 69(9), 991–1001.

    Google Scholar 

  • Lang, S. (2008). Object-based image analysis for remote sensing applications: Modeling reality—Dealing with complexity. In T. Blaschke, S. Lang, & G. J. Hay (Eds.), Object-based image analysis—Spatial concepts for knowledge-driven remote sensing applications (pp. 3–28). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Moller-Jensen, L. (1990). Knowledge-based classification of an urban area using texture and context information in Landsat-TM imagery. Photogrammetric Engineering and Remote Sensing, 56(6), 899–904.

    Google Scholar 

  • Moller-Jensen, L. (1997). Classification of urban land cover based on expert systems, object models and texture. Computers, Environment and Urban Systems, 21(3–4), 291–302.

    Article  Google Scholar 

  • Moller-Jensen, L., Kofie, R. Y., & Yankson, P. W. K. (2005). Large-area urban growth observations—A hierarchical kernel approach based on image texture. Danish Journal of Geography, 105(2), 39–48.

    Article  Google Scholar 

  • Myint, S. W. (2003). The use of wavelets for feature extraction of cities from satellite sensor images. In V. Mesev (Ed.), Remotely sensed cities (pp. 109–134). London: Taylor & Francis.

    Google Scholar 

  • Myint, S., & Lam, N. S. N. (2005). A study of lacunarity based texture analysis approaches to improve urban image classification. Computers, Environment and Urban System, 29, 501–523.

    Article  Google Scholar 

  • Netzband, M., Stefanov, W. L., & Redman, C. L. (2007). Remote sensing as a tool for urban planning and sustainability. In M. Netzband, W. L. Stefanov, & C. L. Redman (Eds.), Applied remote sensing for urban planning, governance and sustainability (pp. 1–24). Berlin/Heidelberg: Springer.

    Chapter  Google Scholar 

  • Pacifici, F., Chini, M., & Emery, W. J. (2009). A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification. Remote Sensing of Environment, 113, 1276–1292.

    Article  Google Scholar 

  • Pesaresi, M., & Bianchin, A. (2001). Recognizing settlement structure using mathematical morphology and image texture. In J.-P. Donnay, M. Barnsley, & P. Longley (Eds.), Remote sensing and urban analysis (pp. 55–68). London: Taylor & Francis.

    Chapter  Google Scholar 

  • Stow, D., Lopez, A., Lippitt, C., Hinton, S., & Weeks, J. R. (2007). Object‐based classification of residential land use within Accra, Ghana based on QuickBird satellite data. International Journal of Remote Sensing, 28(22), 5167–5173.

    Article  Google Scholar 

  • Stow, D., Lipitt, C., & Weeks, J. R. (2010). Geographic object-based delineation of neighborhoods of Accra, Ghana using QuickBird satellite imagery. Photogrammetric Engineering and Remote Sensing, 76(8), 907–914.

    Google Scholar 

  • Sudhira, H. S., Ramachandra, T. V., & Jagadish, K. S. (2004). Urban sprawl: Metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformatics, 5, 29–39.

    Article  Google Scholar 

  • Tzotsos, A., Iosifidis, C., & Argialas, D. (2008). A hybrid texture-based and region-based multi-scale image segmentation algorithm. In T. Blaschke, S. Lang, & G. J. Hay (Eds.), Object-based image analysis—Spatial concepts for knowledge-driven remote sensing applications (pp. 221–236). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Weber, C. (2001). Urban agglomeration delimitation using remote sensing data. In J.-P. Donnay, M. Barnsley, & P. Longley (Eds.), Remote sensing and urban analysis (pp. 145–159). London: Taylor & Francis.

    Chapter  Google Scholar 

  • Weeks, J. R., Hill, A. G., Stow, D., Getis, A., & Fugate, D. (2007). Can we spot a neighborhood from the air? Defining neighborhood structure in Accra, Ghana. GeoJournal, 69, 9–22.

    Article  Google Scholar 

  • Yankson, P. W. K., Kofie, R., & Møller-Jensen, L. (2007). Monitoring urban growth: Urbanization of the fringe areas of Accra. Bulletin of the Ghana Geographical Association, 24, 1–12.

    Google Scholar 

  • Yeboah, I. E. A. (2003). Demographic and housing aspects of structural adjustment and emerging urban form in Accra, Ghana. Africa Today, 50(1), 106–119.

    Article  Google Scholar 

Download references

Acknowledgments

The author wishes to thank Dr. Richard Y. Kofie and Dr. Albert N.M. Allotey of CSIR, Accra, for on-going support and rewarding collaboration for many years, as well as Professor Paul W. K. Yankson at the University of Ghana for contributing with expert advice concerning the urban development of Accra. Furthermore, the author wishes to acknowledge the Danish Development Agency, Danida, for long-term support to research and capacity building collaboration between University of Copenhagen and University of Accra.

Author information

Authors and Affiliations

  1. Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350, Copenhagen, Denmark

    Lasse Møller-Jensen

Authors
  1. Lasse Møller-Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lasse Møller-Jensen .

Editor information

Editors and Affiliations

  1. , Department of Geography, San Diego State University, Storm Hall 308, San Diego, 92182-4493, California, USA

    John R. Weeks

  2. , Faculty of Social and Human Sciences, University of Southampton, Murray Building, Highfield 58, Southampton, SO17 1BJ, United Kingdom

    Allan G. Hill

  3. , Dept of Geography and Regional Studies, University of Miami, Memorial Drive 1000, Coral Gables, 33124-2221, Florida, USA

    Justin Stoler

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media Dordrecht.

About this chapter

Cite this chapter

Møller-Jensen, L. (2013). Methods for Texture-Based Classification of Urban Fringe Areas from Medium and High Resolution Satellite Imagery. In: Weeks, J., Hill, A., Stoler, J. (eds) Spatial Inequalities. GeoJournal Library, vol 110. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6732-4_5

Download citation

Publish with us

Policies and ethics

Search

Navigation