中文摘要：

以IKONOS多光谱影像为例,提出了一种从高分辨率遥感影像提取城市主要道路的方法。首先,利用矢量图像梯度算法获取道路的边缘。然后,通过分析各类地物在IKONOS多光谱波段中的光谱特征,发现绿光与近红外波段的差值影像,不仅能较为有效地区分道路与植被、水体和裸土的信息,而且能减小道路建筑材质不同引起的道路光谱信息的异质性。再利用旋转不变Gabor小波方法获取影像的纹理特征,进一步区分道路与建筑物。在水平集理论框架下,用速度函数将道路的梯度、光谱、纹理特征结合起来,用快速行进算法提取道路。最后,用数学形态学方法进行提取结果的优化。为验证上述方法的实用性,将该方法应用于两个云覆盖情况不同的实验区,结果表明用该方法能有效地从IKONOS影像中提取主要道路信息,且在薄云覆盖区域依然有效。

英文摘要：

A method for urban major road information extraction from IKONOS imagery is proposed.Firstly,a gradient algorithm based on vector-valued images is used to calculate gradients of the IKONOS image of Fuzhou city with four multi-spectral bands.Secondly,spectral signatures of road,bare soil,building,vegetation and water from the IKONOS image are analyzed,and it follows that the road can be effectively distinguished from bare soil,vegetation and water in the differencing image between green and near-infrared bands.In addition,the road variance induced by different constructional materials is reduced in this differencing image than in the original multi-spectral bands.In order to further distinguish between road and building,a rotation-invariant Gabor wavelet method is used to obtain texture features.Accordingly,the gradient and spectral-textural features of the road are integrated to develop a velocity function.Finally,the fast marching algorithm is used to extract major roads and a mathematical morphology method is used to optimize the above extraction results.This proposed method is used to extract major roads from two test areas of Fuzhou city with different cloud conditions.The experiment results show that this method is effective to extract major roads from IKONOS imagery even with thin cloud coverage.