By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ouma, Y.O. ; Tateishi Lab., Chiba Univ., Chiba, Japan ; Tateishi, R. ; Sri-Sumantyo, J.T.

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.

Published in:

Image Processing, IET  (Volume:4 ,  Issue: 4 )