基于TerraSAR-X和Landsat影像的城镇用地提

发布时间：2018-01-10 01:29

本文关键词：基于TerraSAR-X和Landsat影像的城镇用地提取——以常州市为例　出处：《计算机工程与应用》2017年05期 　论文类型：期刊论文

【摘要】：城市扩张作为一个重要的社会和经济现象,正以前所未有的速度和规模在世界各地不断推进。但是盲目的城镇扩张会对社会、生态及经济等产生一定的影响。因此准确高效地提取城镇用地,为城镇规划等提供决策依据变得尤为重要。以常州市为例,对基于单极化Terra SAR-X影像提取建成区方法进行了改进和完善,结合光学卫星影像辅助提取城镇范围,以提高这一类城市城镇提取的精度。该方法首先通过分析局部散斑特性和强度信息,采用基于阈值的方法提取城市和非城市区域,然后再利用Landsat8图像,采用最大似然法和分类后处理来提取城镇范围内的拆迁区域,最后结合提取的城市区域和拆迁区域来完成城镇用地的提取。最后,该研究中,利用Google Earth的遥感影像图进行交叉验证生成混淆矩阵来评价城镇用地的提取精度,其总体分类精度约为89%,表明该方法行之有效。
[Abstract]:As an important social and economic phenomenon, urban expansion is advancing at an unprecedented speed and scale all over the world. But blind urban expansion will affect society. Ecological and economic impact. Therefore accurate and efficient extraction of urban land to provide decision-making basis for urban planning become particularly important. Take Changzhou as an example. The method of building area extraction based on unipolar Terra SAR-X image is improved and perfected, and the method of extracting town area is improved by using optical satellite image. In order to improve the precision of the extraction of this kind of cities and towns, the method firstly analyzes the local speckle characteristics and intensity information, and uses the threshold-based method to extract urban and non-urban areas. Then using the Landsat8 image, the maximum likelihood method and classification post-processing are used to extract the urban demolition area. Finally, combined with the extraction of urban areas and demolition areas to complete the extraction of urban land. Finally, in this study. The extraction accuracy of urban land is evaluated by cross-validation and confusion matrix of Google Earth remote sensing images. The overall classification accuracy is about 89, which shows that this method is effective.
【作者单位】：南京大学地球科学与工程学院;
【基金】：国家自然科学基金面上项目(No.41372353)
【分类号】：F299.27;TP79
【正文快照】： 1引言在全球土地资源开发利用环境下,城市扩张作为一个重要的社会和经济现象,正以前所未有的速度和规模在世界各地不断推进,是最具动态变化性的过程之一[1-2],同时也是推动土地利用/土地覆盖变化(LUCC)的主要力量[3]。中国,作为世界第一大发展中国家,目前正处于城镇化加速推

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