采用非线性尺度空间滤波和SIFT的遥感影像配准方法

针对传统点特征匹配算法存在运算时间长和配准精度低的问题,提出一种基于非线性尺度空间滤波和尺度不变特征转换(SIFT)点特征配准算法.首先,通过非线性尺度空间滤波对基准影像和待配准影像分别进行预处理,保留其边缘信息并去除噪声.其次,采用SIFT算法对预处理后的两幅影像进行特征点提取,通过最近邻和次近邻的欧式距离比值法进行双向匹配,得到匹配特征点.最后,对待配准影像进行仿射变换.结果表明:该方法的总体运行时间比传统SIFT点特征配准算法降低63.2%,且配准精度大幅提高.

Remotely Sensed Imagery Registration Based on Nonlinear Scale-Space Filtering and SIFT

To solve the problems of long executing time and low registration accuracy of the traditional point feature matching algorithm, this article proposed an improved scale-invariant feature transform(SIFT)point feature matching approach based on the nonlinear scale-space filtering. Firstly, the reference image and the to-be-registered one were respectively preprocessed with the nonlinear scale-space filter filtering. Secondly, feature points were extracted from the two images by means of the SIFT algorithm. Then, matched feature points were obtained through a bilateral matching by the ratio of Euclidean distances of the nearest neighbor to that of the next nearest one. Finally, an affine transformation was carried out to the to-be-registered image. Experimental results show that the executing time of the proposed method reduces 63.2% compared with the traditional SIFT point feature matching algorithm, and the registration accuracy is significantly improved.