Variogram-Derived Measures of Textural Image Classification

Application to large-scale vegetation mapping
  • A. Jakomulska
  • K. C. Clarke
Part of the Quantitative Geology and Geostatistics book series (QGAG, volume 11)


Traditional elements of image interpretation include characteristics of first order (tone/color), second order (spatial arrangement: size, shape and pattern) and third order (height, shadow). In digital remote sensing third order image characteristics are considered a nuisance, while potentially useful spatial information has been usually ignored, due to lack of methodology and computational limitations. However few researchers have undertaken integration of spatial and spectral information for image classification: variogram-derived texture has been recently proved to increase the accuracy. The objective of this study was to assess the potential of variogram-derived texture measures applied to classification of high ground resolution imagery. The study was conducted using ADAR images acquired over the Santa Monica Mountains, dominated by chaparral vegetation. Textural parameters were derived from a moving geographic window (of a size determined by the range), as opposed to the commonly applied geometric window of a fixed size. Binary decision tree was used to assess the potential of texture derived from variograms, cross variograms and pseudo-cross variograms, to discriminate between land cover classes. Finally, images were classified using the most significant texture measures and the accuracy was compared with the accuracy of standard per-pixel classification. Overall classification accuracy increased by as much as 15%. Accuracy of homogeneous classes did not change, while significant increase was reported for highly textured classes. Further methods of improving accuracy using variogram-derived texture were discussed.


Remote Sensing Image Classification Land Cover Class Variogram Model Binary Decision Tree 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Berberoglu S., Lloyd C. D., Atkinson P. M., Curran P. J., 2000, The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean, Computers & Geosciences, 26, 385–396CrossRefGoogle Scholar
  2. Carr J. R., 1998, The semivariogram in comparison of the co-occurrence matrix for classification of image texture, IEEE Transactions on Geoscience and Remote Sensing, 36 (6)Google Scholar
  3. Chica-Olmo M., Abarca-Hernández F., 2000, Computing geostatistical image texture for remotely sensed data classification, Computers & Geosciences, 26, 373–383CrossRefGoogle Scholar
  4. Clarke K. C. and Schweizer D. M., 1991, Measuring the Fractal Dimension of Natural Surfaces Using a Robust Fractal Estimator, Cartography and Geographic Information Systems, Vol. 18, No. 1, pp. 37–47CrossRefGoogle Scholar
  5. De Cola L., 1993, Multifractals in Image Processing and Process Imaging, In: Lam N. N.-S., Fractals in Geography, Prentice HallGoogle Scholar
  6. Franklin S. E., McDermid G. J., 1993, Empirical relations between digital SPOT HRV and CASI spectral response and lodgepole pine (Pinus contorta) forest stand parameters, Int. J. Remote Sensing, Vol. 14, No. 12, 2331–2348CrossRefGoogle Scholar
  7. Franklin S. E., Wulder M. A., La vigne M. B., 1996, Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis, Computers & Geosciences, Vol. 22, No. 6, 665–673CrossRefGoogle Scholar
  8. Gardner M., 1997, Mapping Chaparral with AVIRIS using Advanced Remote Sensing Techniques, Unpublished Masters Thesis, University of California Santa Barbara, pp. 58Google Scholar
  9. Miranda F. P., Macdonald J. A., Carr J. R., 1992, Application of the semivariogram textural classifier (STC) for vegetation discrimination using SIR-B data of Borneo, International Journal of Remote Sensing, Vol. 13, No. 12, 2349–2354CrossRefGoogle Scholar
  10. Miranda F. P., Fonseca L. E. N., Carr J. R., Raranik J. V., 1996, Analysis of JERS-1 (Fuyo-1) SAR data for vegetation discrimination in northwestern Brazil using the semivariogram textural classifier (STC), International Journal of Remote Sensing, Vol. 17, No. 17, 3523–3529CrossRefGoogle Scholar
  11. Ramstein G. and Raffy M., 1989, Analysis of the structure of radiometric remotely-sensed images. International Journal of Remote Sensing, 10, 1049–1073CrossRefGoogle Scholar
  12. Roberts D.A., Gardner M.E., Church R., Ustin S.L., Scheer G., and Green R.O., 1998, Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models, Remote Sensing of EnvironmentGoogle Scholar
  13. St-Onge B. A., Cavayas F., 1995, Estimating Forest stand Structure from High Resolution Imagery using the Directional variogram, International Journal of Remote Sensing, Vol. 16, No. 11, 1999–2021CrossRefGoogle Scholar
  14. Wallace C. S. A., Watts J. M., Yool S. R., 2000, Characterizing the spatial structure of vegetation communities in the Mojave Desert using geostatistical techniques, Computers & Geosciences 26 (2000) 397–410CrossRefGoogle Scholar
  15. Woodcock C. E. et al., 1988a, The use of variograms in remote sensing: I real digital images, Remote Sensing of Environment 25:323–348CrossRefGoogle Scholar
  16. Woodcock C. E. et al., 1988b, The use of variograms in remote sensing: II real digital images, Remote Sensing of Environment 25:349–379CrossRefGoogle Scholar
  17. Wulder M. A., Lavigne M. B., LeDrew E. F., Franklin S. E., 1997, Comparison of texture algorithms in the statistical estimation of LAI: first-order, second-order, and semivariance moment texture (SMT), Canadian Remote Sensing Society Annual Conference, GER’97, Geomatics in the Era of Radarsat, May 24–30, 1997, Ottawa, CanadaGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • A. Jakomulska
    • 1
  • K. C. Clarke
    • 2
  1. 1.Faculty of Geography and Regional Studies, Remote Sensing of Environment LaboratoryUniversity of WarsawWarsawPoland
  2. 2.Department of Geography and National Center for Geographic Information and Analysis University of CaliforniaSanta BarbaraUSA

Personalised recommendations