基于改进SIFT的多光谱图像匹配算法

Multispectral Image Matching Algorithm Based on Improved SIFT

针对多光谱图像在各谱段匹配时需要兼顾速度与精度的问题,文中从以下几个方面对SIFT算法进行了改进。针对SIFT算法中特征描述子的维数过高而导致的匹配速度过慢、匹配率低等问题,通过改进特征描述子的结构来实现对描述子的降维。在SIFT特征匹配方面,根据Hessian矩阵的迹的正负确定特征点是极大值点还是极小值点,为后续特征向量匹配缩小搜索范围;然后根据特征点的位置信息剔除部分匹配点对。实验结果表明,改进算法不仅保留了SIFT算法对旋转和亮度等不变性的优势,而且能够有效减少运行时间,并在一定程度上提高了匹配率。

In order to solve the problem that the speed and accuracy need to be taken into account simultaneously when conducting multispectral image matching,this paper improved the SIFT algorithm from the following several aspects.Aiming at the problems such as the slow matching speed and low matching rate caused by high dimension of feature descriptors,this paper improved the structure of feature descriptors to reduce the dimensions of descriptors.In the aspect of SIFT feature matching,firstly,the feature point is determined as the maximum point or minimum point according to the trace of Hessian matrix,which can narrow subsequent search range for the feature vector matching.Then,the partial matching point pairs are eliminated based on the position information of feature points.The experimental results show that the improved algorithm not only preserves the invariance advantages of the traditional algorithm,such as rotation and brightness,but also can effectively reduce the running time,and improve the matching rate on a certain extent.