Multi-scale texture analysis for urban land use/cover classification using high spatial resolution satellite data

Youjing Zhang; Liang Chen; Bing Yu

doi:10.1117/12.761232

26 July 2007 Multi-scale texture analysis for urban land use/cover classification using high spatial resolution satellite data

Youjing Zhang, Liang Chen, Bing Yu

Author Affiliations +

Proceedings Volume 6752, Geoinformatics 2007: Remotely Sensed Data and Information; 67523G (2007) https://doi.org/10.1117/12.761232
Event: Geoinformatics 2007, 2007, Nanjing, China

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.

Citation Download Citation

Youjing Zhang, Liang Chen, and Bing Yu "Multi-scale texture analysis for urban land use/cover classification using high spatial resolution satellite data", Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67523G (26 July 2007); https://doi.org/10.1117/12.761232

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