中文摘要：

道路提取作为典型的线状目标提取,是遥感影像目标解译的研究热点。合成孔径雷达（SAR）影像包含了丰富的物理特性,能够全天时、全天候地获取影像数据,已广泛应用于道路提取中。传统的道路提取方法分为全自动和半自动方法。全自动道路提取会出现漏检和错检,需要大量的人工后处理。半自动方法结合人工干预,是对计算机的计算能力和人工解译准确性的有效折中。提出了用一种改进剖面匹配和扩展卡尔曼滤波（EKF）的方法对SAR影像道路进行半自动提取的方法。首先构建了道路提取模型,其次通过改进剖面匹配算法获取准确的观测值,最后利用EKF对观测值进行更新获取道路最优估计值。选取美国缅因州Howland地区L波段UAVSAR数据和海南陵水地区X波段机载SAR数据进行实验,结果表明,该方法在较少人工干预的情况下,能够对复杂场景道路进行有效稳健的提取。

英文摘要：

As one of typical linear targets,road extraction is the hot spot of remote sensing image interpretation.Accurately and efficiently extracting roads from Synthetic Aperture Radar（SAR）images is of great significance.SAR which can not only acquire data regardless of weather and time,but also provide valuable information on geophysical parameters widely used in road extraction.The traditional method of road extraction based on automatic and semi-automatic method.Because of the complexity of the roads,such as disturbing of the trees along or cover the road,building to cover,cars getting on the road et al,automatic method have some missed or erroneous and need abundant post-processing.The semi-automatic methods that interact with a human operator are considered to be a good compromise between the calculation speed of a computer algorithm and the interpretation skills of the operator.In this paper,a novel semi-automatic road extraction method based on improved profile matching and extended Kalman filtering（EKF）using SAR imagery is introduced.In our method,a road extraction model is built firstly.And then accurate observations are obtained through improved profile matching.Finally,EKF is adopted to update the observations to get the optimal estimates of the road.The effectiveness and steadiness of the proposed method is demonstrated through two experiments using data of Howland,Maine by UAVSAR in L-band and data of Lingshui,Hainan by airborne SAR in X-band.The results of road extraction show that the proposed method is effective,less human intervention,and accurate in the some special scene road of SAR imagery.