中文摘要：

提出了一种基于信号理论和网格化的尺度不变特征变换（SG-SIFT）光学遥感图像配准算法.根据高斯差分尺度空间中各图像层间的频域关系设定各图像提取特征点的数目,使特征点在尺度域上分布均匀;再将各图像层网格化,使特征点在图像空间中分布均匀;然后用一致性检测法剔除有明显错误的匹配对.实验结果表明,利用SG-SIFT算法得到的特征点比尺度不变特征变换（SIFT）算法的特征点分布更均匀,正确匹配对数目比均匀鲁棒尺度不变特征变换（UR-SIFT）算法均多17.47%,且SG-SIFT算法的均方误差明显低于SIFT和UR-SIFT算法.

英文摘要：

A new optical remote sensing image registration method signal gridding-scale invariant feature transform（ SG-SIFT） based on signal theory and gridding is proposed. According to relationships among the image layers in the difference of Gaussians scale space,the feature points＇ number of each image is set in proportion to make the their distribution uniform in the scale space. In addition,a regular gridding method is introduced to achieve the well distribution of feature points in the image space. Then,error matching pairs are eliminated by a correspondence error checking. Statistical and visual results show that SG-SIFT is superior to standard scale invariant feature transform（ SIFT） according to the feature points distribution,while the number of correct matching pairs from SG-SIFT is 17. 47% more than that of uniform robust-scale invariant feature transform（ UR-SIFT） in average and the evaluation indicator of rootmean square error confirms its superior performance to SIFT and UR-SIFT.