首页> 外文会议>22nd Annual Canadian Remote Sensing Symposium Aug 21-25, 2000, Victoria, British Columbia, Canada >Use of a Structural Measure for Urban Expansion Analysis from Landsat TM data
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Use of a Structural Measure for Urban Expansion Analysis from Landsat TM data

机译:利用Landsat TM数据进行城市扩展分析的结构性测度

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摘要

Road density provides meaningful structural information for urban development analysis. This paper investigates the integration of road density structural measure with Landsat TM spectral information for urban area change detection. Beijing, the Chinese capital, serves as the study area, where there have been great changes in the last two decades. Two Landsat TM images used in this study were acquired in the same season from 1984 and 1997. To reduce the spectral confusion between urban 'built-up' and rural 'non built-up' land cover categories, we propose a new structural method using road density combined with spectral bands for post-classification comparison change detection. Road density maps for both dates were produced using a Gradient Profile Direction Analysis (GDPA) algorithm and then integrated with spectral bands. The results from the combined spectral-structural datasets were evaluated and compared with the results from datasets of spectral bands alone. Our study shows that the addition of road density information greatly reduced spectral confusion and increased the accuracy of land cover classification, which in turn improved the change detection results.
机译:道路密度为城市发展分析提供了有意义的结构信息。本文研究了道路密度结构测度与Landsat TM光谱信息的集成,以用于城市区域变化检测。学习区是中国的首都北京,过去二十年来发生了很大的变化。本研究中使用的两个Landsat TM图像是在1984年和1997年的同一季节获得的。为了减少城市“建成”和农村“非建成”土地覆盖类别之间的光谱混淆,我们提出了一种新的结构方法道路密度结合光谱带,用于后分类比较变化检测。使用渐变轮廓方向分析(GDPA)算法生成两个日期的道路密度图,然后将其与光谱带集成。对来自组合光谱结构数据集的结果进行了评估,并将其与仅光谱带数据集的结果进行了比较。我们的研究表明,道路密度信息的添加大大减少了光谱混乱,提高了土地覆盖分类的准确性,从而改善了变化检测结果。

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