Automated urban land-use classification with remote sensing
Automated urban land-use classification with remote sensing
复制标题
利用遥感自动进行城市土地利用分类
DOI：
10.1080/01431161.2012.714510
复制
发表时间：
2013-01-01
影响因子：
3.4
通讯作者：
Wang, Le
中科院分区：
文献类型：
--
作者：
Hu, Shougeng;Wang, Le
城市土地利用信息在各种规划和环境管理过程中发挥着关键作用。本研究的目的是开发一种利用遥感数据对城市土地利用类型进行自动分类的方法。从相关遥感数据获得的7个土地利用地块属性被纳入到四个土地利用类别的分类中，即办公、工业、市政和交通，从以前的研究中报告这些类别是最难分类的。一项实验是在德克萨斯州奥斯汀的一个研究地点进行的。决策树方法的总体准确率为61.68%，kappa系数为0.54。在所采用的所有变量中，建筑面积和建筑高度是影响最大的因素。此外，容积率变量在7个变量中起着次要的作用，说明建筑物的综合水平和垂直属性及其相关的空间特征在区分四类中具有重要意义。
Urban land-use information plays a key role in a wide variety of planning and environmental management processes. The purpose of this study was to develop an automatic method for classifying detailed urban land-use classes with remote-sensing data. Seven land-use parcel attributes, derived from relevant remote-sensing data, were incorporated for classifying four land-use classes, namely office, industrial, civic, and transportation, which were reported as the most difficult ones to classify from previous studies. An experiment was carried out in a study site in Austin, Texas. An overall accuracy of 61.68% and a kappa coefficient of 0.54 were achieved with a decision tree method. Building area and building height turned out to be the most influential factors among all the adopted variables. In addition, the variable of floor area ratio played the second dominant role among the seven variables, demonstrating that synthesized horizontal and vertical properties of buildings and their relevant spatial characteristics are important in differentiating the four classes.