
Proceedings Paper
Multi-scale texture analysis for urban land use/cover classification using high spatial resolution satellite dataFormat | Member Price | Non-Member Price |
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Paper Abstract
An approach of the multi-scale texture classification for urban land cover /use using high-spatial resolution satellite
imagery was proposed in this paper, in which the decision tree classifier was employed. The comparison with the band to
be extraction was performed for three images. The grey-level co-occurrence matrix was adopted to calculate texture
values of twenty windows. The J-M distance was used to optimize the texture scales for the eight classes of land cover
/use. It was founded that maximum J-M distance appears in the window 15×15 for broadleaf-evergreen, conifer, 27×27
for grass land, 47×47 for bare soil, 67×67 for building and water, respectively. The experimental results showed that
overall accuracy with multi-scale texture was 81.7% for eight urban types. The comparison with both the single scale
texture and original spectrum showed that the overall accuracy of multi-scale texture was higher than ~6% of the single
scale texture and ~11% of the original spectrum respectively. The results also indicate that multi-scale texture method is
more accurate and reasonable with real world, and can reduce the "salt-and-pepper" effect. This is achieved by the
proposed method, in which the classification with optimization the texture scales is of the most critical value for
mapping urban land cover/use using high spatial resolution satellite image.
Paper Details
Date Published: 26 July 2007
PDF: 10 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67523G (26 July 2007); doi: 10.1117/12.761232
Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information
Weimin Ju; Shuhe Zhao, Editor(s)
PDF: 10 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67523G (26 July 2007); doi: 10.1117/12.761232
Show Author Affiliations
Bing Yu, Hohai Univ. (China)
Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information
Weimin Ju; Shuhe Zhao, Editor(s)
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