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30 October 2009 Research on fractal model of urban land use considering the appropriate spatial resolution for remote sensing imagery
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Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 749816 (2009) https://doi.org/10.1117/12.832432
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
The spatial resolution is an important measure about spatial scales, which affects the accuracy of interpretation for remote sensing imagery and furtherer leads to some serious uncertain problems on fractal model of urban land use. In this paper, the average local variance model based on spatial sampling method is used to select the appropriate spatial resolution in order to improve fractal model of urban land use. The information entropy dimension is proposed to quantitatively express spatial balance for a certain urban land use type. An example of application research is experimented in Wuchang district through QuickBird remote sensing imagery in 2002. By scaling up with the initial spatial resolution, the appropriate spatial resolution is 10m in round numbers. The information entropy dimension of built-up area and water are 1.921 and 1.907, which are larger and imply more homogeneously spatial distribution. But the information entropy dimension of farmland and unused land are 1.291 and 1.218, which are lower and imply more concentrated spatial distribution. The results suggest that the average local variance is very advantageous to provide the appropriate resolution for remote sensing imagery, which can greatly improve the accuracy of interpretation in extracting feature information of urban land use.
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Hao Wu, Yan Li, Qingqing Li, and Xiaoling Chen "Research on fractal model of urban land use considering the appropriate spatial resolution for remote sensing imagery", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749816 (30 October 2009); https://doi.org/10.1117/12.832432
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