高分辨率遥感影像城市房屋信息自动提取模型与实验研究

分析高分辨率遥感影像Quick Bird上房屋信息特征空间,构建Quick Bird影像房屋空间语义模型,并以大庆市东城区为实验区域,利用窗口阈值法对影像进行区域分割后,通过空间语义模型实现Quick Bird影像房屋信息的自动提取,并将提取结果与实地采样数据进行精度对比。结果显示,其形状一致性和面积一致性分别达到89.5%和88%,其中,矩形房屋提取效果最佳,人字形、多边形房屋的提取效果较理想,但圆形房屋提取效果不理想。

Study on Urban House Information Automation Extraction Model and Its Experiment of High Resolution Remote Sensing Images

Being the important urban basic geography information,the automatic recognition and accurate extraction of the house are meaningful for many applications such as getting GIS data,image understanding and mapping with big scale and so on.This paper mainly discusses the construction of house semantic model for high resolution image Quick Bird and the realization of the automatic extraction through complete spatial semantic expression.Based on the introduction to the characters and constructing flow of space semantic model,the feature space and context of house information in high resolution remote sensing image are analyzed,and the house semantic network model of Quick Bird image is also constructed in this paper.Furthermore,the accuracy and practicability of space semantic model are checked up through extracting house information automatically from Quick Bird image after extracting candidate semantic nodes to the image by taking advantage of grey division method and window threshold value method.The sample results indicate that its shape consistency and area consistency are 89.5% and 88% respectively.Thereinto,the effect of the extraction of the houses with rectangular roof is the best and that with herringbone and the polygonal roofs is just ideal.However,the effect of the extraction of the houses with round roof is not satisfied and thus they need the further perfection to the semantic model to make them own higher applied value.