The composition and arrangement of spatial entities, i.e., land cover objects, play a key role in distinguishing land use types from very high resolution (VHR) remote sensing images, in particular in urban environments. This paper presents a new method to characterize the spatial arrangement for urban land use extraction using VHR images. We derive an adjacency unit matrix to represent the spatial arrangement of land cover objects obtained from a VHR image, and use a graph convolutional network to quantify the spatial arrangement by extracting hidden features from adjacency unit matrices. The distribution of the spatial arrangement variables, i.e., hidden features, and the spatial composition variables, i.e., widely used land use indicators, are then estimated. We use a Bayesian method to integrate the variables of spatial arrangement and composition for urban land use extraction. Experiments were conducted using three VHR images acquired in two urban areas: a Pleiades image in Wuhan in 2013, a Superview image in Wuhan in 2019, and a GeoEye image in Oklahoma City in 2012. Our results show that the proposed method provides an effective means to characterize the spatial arrangement of land cover objects, and produces urban land use extractions with overall accuracies (i.e., 86% and 93%) higher than existing methods (i.e., 83% and 88%) that use spatial arrangement information based on building types on the Pleiades and GeoEye datasets. Moreover, it is unnecessary to further categorize the dominant land cover type into finer types for the characterization of spatial arrangement. We conclude that the proposed method has a high potential for the characterization of urban structure using different VHR images, and for the extraction of urban land use in different urban areas.

空间实体（即土地覆盖物）的组成和排列在将土地使用类型与超高分辨率（VHR）遥感影像区分开来方面，尤其是在城市环境中，起着关键作用。本文提出了一种新的方法来表征使用VHR图像提取城市土地利用的空间布局。我们导出一个邻接单位矩阵来表示从VHR图像获得的土地覆盖物的空间布置，并使用图卷积网络通过从邻接单位矩阵中提取隐藏特征来量化空间布置。然后估计空间布置变量（即隐藏特征）的分布以及空间构成变量（即广泛使用的土地利用指标）。我们使用贝叶斯方法对城市土地利用提取的空间布局和构成变量进行整合。使用在两个市区获得的三幅VHR图像进行了实验：2013年在武汉的a宿星图像，2019年在武汉的Superview图像和2012年在俄克拉荷马城的GeoEye图像。我们的结果表明，该方法提供了有效的手段来表征土地覆盖物的空间布置，并产生城市土地利用摘要，其总体准确度（即86％和93％）高于使用基于建筑物类型的空间布置信息的现有方法（即83％和88％）在the宿星和GeoEye数据集上。此外，没有必要将主要的土地覆盖类型进一步分类为更精细的类型以表征空间布置。