POI辅助下的高分辨率遥感影像城市建筑物功能分类研究

城市建筑物是城市的重要组成部分,对城市建筑物进行功能分类可以为城市功能区划分提供有利依据,辅助政府部门对城市规划、土地利用、资源、人口等方面的分布与分配进行管理与决策,有助于推进城镇化建设的可持续发展。仅利用目前的遥感分类技术难以对高分辨率遥感影像的城市建筑物信息进行功能分类,然而将遥感、互联网兴趣点（Point of Interest, POI）数据以及GIS技术有效地结合在一起,可以更为细致地分析城市信息,不仅实现了建筑物功能分类,而且提高了分类的准确率与可信度。本文首先选取卷积神经网络（Convolutional Neural Networks, CNN）方法对高分辨率遥感影像数据进行建筑物提取;然后,对POI数据的城市商服、公建和住宅用地进行核密度分析;最后分别统计每个建筑物3种用地的核密度平均值,并将该值设置为此建筑物的属性值,并结合POI数据的实际情况选择具有最佳功能分类精度的属性值作为阈值提取3种用地信息,从而完成不同功能的城市建筑物分类。精度评价结果表明,该方法对3种用地的提取效果良好,分类精度达到86%以上。

Functional Classification of Urban Buildings in High Resolution Remote Sensing Images through POI-assisted Analysis

As the space for human habitation and activity, urban buildings are an important part of the city. Their renewal and renovation affects development of the city and changes people’s life at all times. Functional classification of urban buildings provides supporting evidence for categorizing urban functional areas, and also helps the government in land use planning, as well as managing the distribution of population and resources, promoting the sustainable development of urban areas. However, current classification technology of remote sensing is insufficient to make functional classification of urban buildings. In this paper, we analyzed urban information in great depth, by classifying the function of urban buildings. The efficiency and precision of the classification is improved after combining remote sensing, the Internet POI (Point of Interest) data and GIS technology. We first chose the method of CNN (Convolutional Neural Networks) to extract building information from remote sensing images of high resolution. The precision of the extraction is above 93% as is shown by precision evaluation. POI data was then classified into 3 types by manual work, namely buildings used for commercial service, public service and residence. The classified POI data were estimated by Kernel Density. After which the mean Kernel Density value of every type of buildings was calculated and these three types of buildings were delimited by thresholds. Thus, buildings for commercial service, public service and residence could be recognized from the building information assisted by POI data, achieving the functional classification of urban buildings. This method has shown good extraction efficiency compared to visual interpretation-the overall accuracy is 86.85% and Kappa Coefficient is 0.8153 according to precision evaluation. In future research, this method can be used to classify and identify different types of urban buildings. However, there are still some problems to be discussed in this method. For example, when defining buildings’ functional types by threshold of Kernel Density, one building may have more than one or no type. Besides, POI data have some limitations when representing the range of different types of buildings: one point may represent either a grand shopping mall or a convenience store. These will be addressed in future studies.