Urban features recognition and extraction from very-high resolution multi-spectral satellite imagery: a micro–macro texture determination and integration framework

Urban features recognition and extraction from very-high resolution multi-spectral satellite imagery: a micro–macro texture determination and integration framework

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study presents the first experimental results on the integration of discrete wavelet transform (DWT) derived contexture (macro-texture) and grey-level co-occurrence matrices (GLCM) (micro-texture) in the recognition and extraction of the following selected urban land cover information from very-high spatial resolution Quickbird imagery: residential buildings, commercial buildings, roads/parking and green vegetation. The DWT filters capture the lower and mid-frequency texture information, whereas the GLCM captures the high-frequency textural components, for the same scene features. Besides the commonly used micro-texture (GLCM), the macro-texture (DWT) is modelled here to take care of the contextual information defined as feature edge (size and shape). This edge information is arguably derived from the multi-scale and multi-directional components of the DWT. From the statistical significance testing of the per-pixel classification accuracy results with the z-score, it was found that the integrated feature sets comprising the Quickbird spectral bands, 3×3 mean-GLCM and the first level of the vertical-DWT sub-band outperformed all the other tested input primitives, with a z-score value of 2.25. The accuracy results showed that all the three feature primitives were essential in improving the recognition and extraction of tested urban land cover in very-high spatial resolution Quickbird imagery.


    1. 1)
      • A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas
    2. 2)
      • Benediktsson, J.A., Arnason, K., Pesaresi, M.: `The use of morphological profiles in classification of data from urban areas', Proc. IEEE/ISPRS Joint Workshop on Remote Sensing over Urban Areas (Urban 2001), November 2001, Rome, Italy, p. 30–34
    3. 3)
    4. 4)
    5. 5)
      • Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery
    6. 6)
    7. 7)
      • The utility of texture analysis to improve per-pixel classifications for high to very high spatial resolution imagery
    8. 8)
      • A texture-based classification method for classifying built areas according to their density
    9. 9)
    10. 10)
    11. 11)
      • Statistical texture characterization from discrete wavelet representations
    12. 12)
    13. 13)
    14. 14)
      • Incorporating texture into classification of forest species composition from airborne multispectral images
    15. 15)
      • Characterization of signals from multiscale edges
    16. 16)
    17. 17)
      • Visual pattern discrimination
    18. 18)
      • Urban spatial pattern analysis from SPOT panchromatic imagery using textural analysis
    19. 19)
      • Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case
    20. 20)
    21. 21)
      • Digital image processing algorithms and applications
    22. 22)
    23. 23)
    24. 24)
    25. 25)
      • On the optimisation and selection of wavelet texture for feature extraction from high-resolution satellite imagery with application towards urban-trees delineation
    26. 26)
      • Evaluation of textural and multipolarization radar features for crop classification
    27. 27)
      • The use of structural information for improving land-cover classification accuracies at the rural-urban fringe
    28. 28)
      • Frequency-based contextual classification and grey-level vector reduction for land-use identification
    29. 29)
      • Robust and efficient detection of salient convex groups

Related content

This is a required field
Please enter a valid email address