基于SIFT与Contourlet变换的高分辨遥感图像配准

针对高分辨遥感图像特征量较多的情况,提出一种基于SIFT与Contourlet变换相结合的图像配准算法。首先将图像进行Contourlet变换分解成低频和高频子带,对高频子带通过设定合适的阈值来提取图像边缘特征点,对低频子带进行SIFT特征点提取。将两者提取到的特征点分别匹配后得到粗匹配点对,利用随机抽样一致性(RANSAC)选择出精匹配点对,实现图像配准。实验表明:在多源遥感图像配准过程中,与基于非采样Contourlet变换(NSCT)和基于SIFT特征提取相比,该算法能够更准确地提取到特征点,具有更高的运算效率以及匹配率。

Registration Technique for High-resolution Remote Sensing Images based on SIFT and Contourlet Transform

For high-resolution remote images with tremendous feature,a new method of image registration is proposed by combining contourlet transformation with SIFT.Firstly,the discrete contourlet transformation decomposes the original image into a low-frequency sub-band and several high-frequency sub-band.For high-frequency sub-bands,image edge feature points are extracted by an appropriate threshold.For the low-frequency sub-band,image SIFT feature are extracted by SIFT.Then,coarse control point pairs are searched out with normalized corss-correlation matching and SIFT matching before accurate control point pairs are hunted by RANSAC.The experimental results show that,as for high-resolution remote sensing image registration,this method can extract more acculately control point pairs,get higher computing efficiency and matching ratio than these methods based on NSCT or SIFT.