GVF Snake与显著特征相结合的高分辨率遥感图像道路提取

高分辨率遥感图像中的道路信息,对地理信息系统数据库的更新具有重要的意义。本文通过分析道路在遥感图像中所呈现的特性,提出了一种基于显著特征和GVF Snake的高分辨率遥感图像道路提取方法。该方法根据视觉认知理论将道路的几何特性和辐射特征作为显著特性。首先,通过融合颜色对比度和空间统计特征计算显著性图,并以输出的显著图的最大值作为GVF Snake的初始种子点;再利用区域生长法求出道路的初始边界,通过梯度矢量流模型的迭代求解,并最小化能量函数,实现道路信息的自动提取。试验结果表明,本文所提出的方法提高了不仅可以提高计算效率,还具有较好的检测精度。

Road Extraction from High-spatial-resolution Remote Sensing Image by Combining GVF Snake with Salient Features

The road information in the high-spatial-resolution remote sensing image is of great significance for updating the GIS database.Through analyzing the road features shown in the remote sensing image,this paper presents a road detection method based on salient features and gradient vector flow (GVF) Snake.According to the visual cognition theory,the road geometric and radiation features are viewed as salient features in this paper.First,the saliency map is calculated by fusing the color-based and structure-based contrasts,the maximum saliency value is regarded as the seeds of the GVF Snake.Then,a region-growing algorithm is applied to compute the initial boundaries,the energy function of the GVF Snake is minimized by iterative solution of the gradient vector flow model to get the final road information.Experimental results show that the proposed method could enhance the computational efficiency and has good detection accuracy.