中文摘要：

目的全球最高30m分辨率的地表覆盖数据GlobeLand30具有高分辨率、高精度等特性，为全球制图提供了重要的数据来源。如何快速准确地识别GlobeLand30与矢量数据中的同名实体，对于空间数据的更新、集成与融合具有重要的意义，针对当前该数据与矢量数据匹配识别方法存在的不足，本文提出一种新的综合相似性度量方法。方法利用面实体轮廓线的多级弦长、拱高和中心距离等特性，构造多级弦长拱高复函数对其整体和细节特征进行描述；然后对面实体轮廓线进行等间隔重采样，通过快速傅里叶变换得到傅里叶描述子对面实体间的形状相似性进行度量；最后将面实体的位置、大小、方向和形状进行加权综合，得到一种综合相似性度量模型。结果将本文综合相似性度量模型应用到GlobeLand30与矢量数据面状水体的匹配中，实验结果为查准率P为100％，查全率Q为97．1％，匹配速度和准确率优于其他文献所提出的方法，当拱高级数为4时，匹配效果达到最优；最后，将该相似性度量模型应用到GlobeLand30数据化简和光滑前后的相似性度量上，也取得了很好的应用效果。结论本文方法适用于GlobeLand30与矢量数据的相似性度量，对于GlobeLand30与矢量数据的集成与融合，对利用GlobeLand30进行矢量数据的生产与更新具有重要的意义。

英文摘要：

Objective Global land cover and its change are indispensable basic information for environmental change research, detection of national geographical conditions, and sustainable development. In 2014, the National Geomatics Center of China produced GlobeLand30, a remote sensing mapping product with the highest resolution （30 m） in the world. This data set exhibits high resolution and high accuracy, and thus, it can satisfy the cartographic requirement of 1：250 000 and other smaller measuring scales as well as provide global data production and updating with significant data resources. When the difference between GlobeLand30 and vector data is considered, accurately and rapidly recognizing identical entities from these two types of data is highly important to update information as well as integrate and fuse multi-source and muhiscale spatial data. To overcome the shortcomings of the method for recognizing and matching GlobeLand30 and vector data, this study proposes a complex function based on a multilevel arc-height. Fourier shape descriptors can be obtained to measure shape similarity among area entities, and a comprehensive similarity measurement model can be estab- lished by integrating the location, size, direction, and shape of area entities. This study uses the proposed model to recog- nize and match GlobeLand30 and vector data as well as to measure the similarity of Globeland30 before and after simplifica- tion and smoothing. Method This method constructs a complex function based on multilevel chord length to describe the en- tire and detailed features of area entities by using the characteristics of their border, such as arc-height and central dis- tance. After resampling the border of area entities, a shape descriptor with an independent and compact initial point on the border can be established through fast Fourier transform, which exhibits invariant properties in terms of translation, rota- tion, and scaling to measure shape similarity and diversity among area entities. Lastly, a comprehensive simi