Advertisement

Land Use Classification using Structural Features

  • Cem Ünsalan
  • Kim L. Boyer
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

After a thorough survey on land use classification, we begin with a set of measures based on straight lines for Ikonos images in this chapter. Subsequent detailed analyses (counting and classifying dwellings, for example) can then be confined to developed areas in the following chapters. Straight line structures will be more prevalent and more organized in developed areas than in wilderness or rural areas. However, for our measures we only need this assumption to hold locally. Four of our most promising measures (based on length and contrast) do not depend heavily on this assumption. On the other hand, our remaining measures (orientation, line spacing, and periodicity) depend on this assumption heavily. As expected, this later group could not perform as well as the length and contrast measures experimentally. Our objective at this stage is the (rough) classification of the image into regions of little or no development (wilderness or rural) and developed regions (urban or residential). We applied Bayes, Parzen window, and nearest neighbor (NN) classifiers to label each image region. Initially, we defined a two-class problem to discriminate “urban” and “not urban” regions and obtained excellent results (roughly 87% correct classification). Although there has been extensive work on land use classification, no structural approaches to this problem have been reported. Our approach, being totally based on straight lines, offers the first such solution, to our knowledge. This approach shows very promising results in extensive testing over a wide variety of land development patterns.

Keywords

Feature Space Classification Performance Near Neighbor Correct Classification Rate Image Window 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    C. Ünsalan, K.L. Boyer, IEEE Trans. Geosci. Remote Sens.42(7), 1575 (2004) CrossRefGoogle Scholar
  2. 2.
    J.B. Burns, A.R. Hanson, E.M. Riseman, IEEE Trans. Pattern Anal. Mach. Intell.8(4), 425 (1986) CrossRefGoogle Scholar
  3. 3.
    S. Sarkar, K.L. Boyer, IEEE Trans. Pattern Anal. Mach. Intell.13(11), 1154 (1991) CrossRefGoogle Scholar
  4. 4.
    S.G. Tan, Image Feature Extraction: Line Detection and Organization. Msc. thesis, The Ohio State University (1990) Google Scholar
  5. 5.
    A.V. Oppenheim, R.W. Schafer, J.R. Buck,Discrete Time Signal Processing, 2nd edn. (Prentice Hall, New York, 1999) Google Scholar
  6. 6.
    R.O. Duda, P.E. Hart, D.G. Stork,Pattern Classification, 2nd edn. (Wiley Interscience, New York, 2001) MATHGoogle Scholar
  7. 7.
    A. Rosenfeld, R.A. Hummel, S.W. Zucker, IEEE Trans. Syst. Man Cybern.6, 420 (1973) MathSciNetCrossRefGoogle Scholar
  8. 8.
    H. Yamamoto, Comput. Vis. Graph. Image Process.10, 256 (1979) CrossRefGoogle Scholar
  9. 9.
    W.A. Sethares, T.W. Staley, IEEE Trans. Pattern Anal. Mach. Intell.47(11), 2953 (1999) MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Electrical and Electronics EngineeringYeditepe UniversityKayisdagiTurkey
  2. 2.Dept. Electrical, Comp. & Systems Eng.Rensselaer Polytechnic InstituteTroyUSA

Personalised recommendations