高速公路路侧景观量化方法  

Feature quantification method for freeway roadside landscape

作　　者：李世武[1] 王琳虹[1] 孙文财[1] 田晶晶[1] 乔飞艳[1] 

机构地区：[1]吉林大学交通学院,长春130022

出　　处：《吉林大学学报（工学版）》2011年第4期956-962,共7页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(50978116)

摘　　要：将路侧景观量化分成图像分割与特征提取两个阶段。第一阶段为路侧景观图像分割。对比分析了标记分水岭算法、Canny算子边缘检测算法及纹理分析算法的分割效果,选定了鲁棒性较强的纹理分析算法进行高速公路路侧景观分割。第二阶段为景观色彩特征与连续性特征的提取。在景观色彩特征提取方面,利用k均值聚类分析法提取了景观的RGB向量值,借鉴蒙赛尔色系划分法建立了色彩划分体系,对景观的宏观色彩进行了判别。结果表明:该方法可客观准确地表征景观的宏观色彩。在景观连续性特征表征方面,选取昆元高速公路部分路段景观提取了基于时间序列的HSV值。HSV值与路侧景观构成及连续性的量化分析表明两者相关性很强,方法快速有效。The quantification process of the roadside landscape can be divided into 2 stages. The first stage is the image segmentation. Three segmentation methods such as marker-based watershed algorithm, Canny operator edge detection algorithm and texture analysis algorithm were comparatively analyzed, and the texture analysis algorithm which is characterized by more robust was selected. The second stage is the feature extraction of color space and continuity. In the extraction of the landscape color space, using the k-mean cluster analysis to extract the RGB vector of landscape, the color segmentation system was established making reference to the Munsell＇s color segmentation method, and the macro-color of the landscape was identified with high accuracy. In the extraction of landscape continuity, taking the landscape of section of Kunyuan freeway as a case example, the HSV values based on time series were extracted. The relationship between the HSV color space and the roadside landscape was analyzed quantitatively and the results show strong correlativity.

关 键 词：交通运输系统安全工程 路侧景观 纹理分析 图像分割 特征量化 K-均值聚类 

分 类 号：U491.2[交通运输工程—交通运输规划与管理]