基于遗传算法的SAR图像道路网检测方法

提出了一种基于遗传算法的SAR图像道路网检测算法.该算法以道路在SAR图像中呈黑色直线状结构为基本出发点,首先检测线特征点以获取潜在道路点;接着利用基于每个连通区域上的Radon变换提取线基元;然后从图像上最长的线基元出发,以其为种子基元,在其周围确定一个搜索区域,用遗传算法选择与种子基元共线的线基元进行连接,并更新种子基元,直到完成所有的连接,得到候选道路段.为了使检测道路更准确,利用蛇模型调整道路段的位置,然后用道路的特征进行鉴别.最后检测道路的交叉点,完成整个道路网的检测.机载SAR图像的实验结果及定量分析均证明了该算法的有效性.

Automatic Extraction of Road Network from SAR Imagery Based on Genetic Algorithm

This paper proposes a new approach for automatic detection of road network from SAR imagery based on genetic algorithm.Since the roads on low resolution SAR imagery can be modeled as dark linear structure,it first detects line feature pixels in order to get potential road pixels.Followed that,extracting primitive line segments are accomplished by Radon Transform on every set of connected pixels.Then it takes the longest primitive line segment as initial seed, searches line segments in a search region around it,and uses Genetic algorithm to select the opti- mum line segments to be grouped to the seed line.This process iterates until all the lines are grouped,and the candidate road segments are got.The snake model is used to adjust the position of the candidate road segments in order to make road segments discrimination using the road's lin- ear feature more effective.Finally,road network is constructed after extracting crossings.The feasibility of the approach is slowed not only by presenting reasonable results but also by evalua- ting them quantitatively based on ground truth.