基于GA的SAR图像中主干道路提取

从高分辨率合成孔径雷达(SAR)图像中提取道路及其他线性特征已成为目前遥感图像信息提取研究的热点。由于高分辨率SAR图像中，目标背景复杂，同时由于受相干斑噪声的影响，因此很难直接从原始图像数据中提取道路特征。为了能够从背景复杂，受斑点噪声干扰的高分辨率SAR图像中准确提取道路，提出了一种利用遗传算法提取主干道路的方法。该方法利用模糊C均值聚类法对滤波后的SAR图像进行无监督聚类，首先将图像分为林地、建筑物、道路等基本类，并将道路类像素从图像中分离出来，使问题得到简化；然后根据道路类像素的隶属度和道路像素灰度值的均匀特性来建立具体的道路模型；最后利用遗传算法搜索全局最优道路。实验结果表明，该方法可以很好地从SAR图像中提取各种主干道路。

Road Extraction in SAR Images Using Genetic Algorithm

More and more attention has been paid to extraction of roads and other linear features from high-resolution synthetic aperture radar(SAR) images. Due to the complicated background of objectives and the speckle noise in the high-resolution SAR images, it is almost impossible to extract roads directly from original remote sensing images. In order to extract roads precisely from high-resolution SAR images with complicated background and speckle noise, a method of using genetic algorithm(GA) is developed to extract main roads in the paper. After the original SAR images are filtered to suppress the speckle noise, Fuzzy C means is used to classify the images unsupervisedly into vegetation, built areas, roads and other class, and the pixels belonging to the main roads are isolated from the images to simplify the original problem. Then, according to the membership of pixels to main roads and the uniformity of gray, a road model is constructed, and the genetic algorithm is used to search globally optimized roads. The experimental results show that the presented approach can effectively extract main roads from high-resolution SAR images.