一种基于改进SIFT的SAR图像配准算法

摘要

为克服SIFT (scale-invariant feature transform)描述子应用于SAR图像配准领域时配准精度低的不足,提出一种基于改进SIFT的SAR图像精确配准算法.该方法首先提取特征点的SIFT描述子和改进的旋转不变纹理化特征描述子,再利用典型相关分析特征融合算法将2种描述子融合,形成新的特征描述子,计算2幅图像中各个特征点间的改进加权距离并通过预先设定好的阈值完成粗匹配,最后通过随机抽样一致性算法去除误匹配实现精匹配,并代入仿射变换模型以求得变换参数,完成图像的配准.仿真结果表明:该方法的匹配性能明显优于SIFT及PCA-SIFT算法,能够有效处理图像在尺度变化、灰度变化、旋转角度变化的情况下SAR图像配准问题,且配准精度达到了亚像素级.%For overcoming the low image registration of the SIFT(scale invariant feature transform) when it is applied to SAR image registration,put forward the SAR image accurate registration algorithm.At first,the method extracts the SIFT descriptor and improved rotation invariant textured feature descriptor,then use canonical correlation analysis feature fusion method to fuse 2 descriptors and create new feature descriptor.Next the improved weighting distance among every feature point in 2 images is calculated and introduced into affine transform model for transform parameter and realize image registering.The simulation results show that registration result of the proposed method under much better than SIFT method and PCA-SIFT,and registration precision reaches sub-pixel level when image in conditions of scale variation,grey scale variation,and rotation angle variation.