Dempster-Shafer证据融合形状特征的高分辨率遥感图像道路信息提取

宁亚辉; 雷小奇; 王功孝; 李顺琴

doi:10.11834/jig.20111213

图像理解和计算机视觉 | 浏览量 : 0 下载量: 254 CSCD: 0

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专辑

Dempster-Shafer证据融合形状特征的高分辨率遥感图像道路信息提取
Road extraction from high-resolution remote sensing images based on Dempster-Shafer evidence theory and fusion shape features
2011年16卷第12期页码：2183-2190
纸质出版：2011
DOI： 10.11834/jig.20111213
稿件说明：

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宁亚辉, 雷小奇, 王功孝, 李顺琴. Dempster-Shafer证据融合形状特征的高分辨率遥感图像道路信息提取[J]. 中国图象图形学报, 2011,16(12):2183-2190. DOI： 10.11834/jig.20111213.

Ning Yahui, Lei Xiaoqi, Wang Gongxiao, Li Shunqin. Road extraction from high-resolution remote sensing images based on Dempster-Shafer evidence theory and fusion shape features[J]. Journal of Image and Graphics, 2011, 16(12): 2183-2190. DOI： 10.11834/jig.20111213.

摘要

道路提取是遥感图像目标识别和提取中一项具有重要意义而困难的任务。在遥感图像道路提取的过程中

由于道路的不同形状和图像信息的复杂性

目前在许多基于形状特征提取道路的方法中

选取形状特征阈值时具有一定的难度

且需要大量的人工干预操作

缺乏一定的通用性

因此

本文提出一种基于DS(dempster-shafer)证据理论和形状特征的道路提取方法。该方法首先对道路的几何形状特征进行分析和优化

据此设计概率分配函数

并利用DS证据理论融合形状特征以获取道路段

最后通过道路连接操作得到道路的中心线。文末通过对典型道路图像和非典型道路图像的实验表明

该方法能够降低选取形状特征阈值的难度和对人工的依赖性

能适用于高分辨率遥感图像中直线型和曲线型道路的提取

具有一定的可行性。

Abstract

Road extraction is one of important issues for target recognition in remote sensing image. At present

most road extraction methods are based on shape-features.But the threshold selection is very difficult and needs much human intervention

because the shape-features have the complicated information in remote sensing images. Hence

a road-extraction method using the DS (Dempster-Shafer)evidence theory to fuse the shape features is proposed. First

some shape features are selected and optimized. Then

the basic probability assignment functions are designed and the road segments are extracted by using shape features with the DS theory. Finally

the road center lines are obtained by connecting road segments.Typical road images and non-typical road images were selected for experiments

and the results prove that the method can reduce the difficulty of threshold selection and the dependency of human intervention.The method is effective for the typical road images and non-typical road images.