目的 路网密度是一个地区交通可达性的重要指标，获取影像中的道路像元是计算路网密度的首要环节。本文利用高分遥感影像中城市主干道路光谱特征沿道路方向趋向一致的特性，提取城市主干道路。方法 通过分析像元点的角度纹理图，根据最小灰度方差所在方向设计2维Gabor滤波器。用滤波值数组作为每个像元的表征，借助k均值聚类分割策略将道路像元提取出来，并细化为主干道路网。结果 以完全率、正确率和提取质量作为精度评价指标，以k均值聚类作为分割策略，当分割对象分别为灰度值、灰度共生矩阵、多通道2维Gabor滤波值及本文采用的特征向量时，评价结果分别为0.45、0.51、0.37，0.62、0.70、0.54，0.58、0.66、0.52及0.72、0.78、0.65；以Hough变换法作为提取策略时，评价结果分别为0.41、0.56和0.34；以多尺度分割法作为提取策略时，评价结果分别为0.41、0.56和0.34。由此可知，采用相同分割策略，本文采用的分割对象相较其他对象可以获得更好的分类精度。相较传统的基于线状或面状特征的分类手段，本文方法在精度方面亦具有一定的优势。结论 本文提出了一种全新的道路像元提取方法，通过构造特定方向的2维Gabor滤波值数组进行影像分割。实验表明该方法具有较好的抗噪性和普适性，广泛适用于GF-1、GF-2、IKONS、QuickBird及其他传感器获取的高分辨率遥感影像。

Objective The density of road network is an important indicator of the accessibility of regional traffic,and obtaining the road pixels in an image is the first step to calculate the density of road network.This study assumes that the spectral characteristic of urban trunk road in high-resolution remote sensing image is consistent along the road direction.Method A 2D Gabor filter is designed on the basis of the analysis on the angular texture diagram of pixel points and the direction of gray minimum variance.The filtered value group is used as a representation of each pixel.The road pixels are extracted by k-mean clustering segmentation strategy and are refined into the trunk road network.Result Completion rate,accuracy,and extraction quality are regarded as evaluation indicators of precision and k-mean clustering is used as segmentation strategy.When the segmentation objects are gray value,gray-level co-occurrence matrix,multichannel 2D Gabor filtered value,and the eigenvector adopted in this study,the evaluation results of the completion rate,accuracy,and extraction quality are 0.45,0.51,and 0.37;0.62,0.70,and 0.54;0.58,0.66,and 0.52;and 0.72,0.78,and 0.65,respectively.When the Hough transformation method is used as extraction strategy,the evaluation results of the completion rate,accuracy,and extraction quality are 0.41,0.56,and 0.34 respectively.When the multiscale segmentation method is used as extraction strategy,the evaluation results of the completion rate,accuracy,and extraction quality are 0.41,0.56,and 0.34,respectively.Therefore,in the case of the same segmentation strategy,the segmentation objects adopted in this study can exhibit higher classification accuracy than that of the other objects.Compared with traditional classification methods based on linear or planar characteristics,the method adopted in this study exhibits a certain advantage in terms of accuracy.Conclusion In this study,a new extraction method of road pixel is proposed,in which the image is segmented by creating a 2D Gabor filtered value group with a specific direction.Experimental results show that this method presents high anti-noise performance and universality and can be applied to the high-resolution remote sensing images from GF-1,GF-2,IKONOS,QuickBird,and other sensors.