基于角点特征的遥感图像快速配准

Fast Registration of Remote Sensing Image Based on Corner Feature

摘要: 针对遥感图像传统配准算法匹配速度较慢、不满足实时性要求等问题，本文提出了一种结合改进AGAST与FREAK算法的遥感图像快速配准方法。首先，利用改进AGAST检测算法分别快速检测参考图像和待配准图像中的特征点；然后用FREAK算法获取二进制描述符串，利用级联匹配计算特征向量之间的汉明距离，获得特征点匹配对；最后利用改进的相似三角形剔除方法去掉错误的匹配对，并结合最小二乘法，估算出空间几何变换参数，实现两幅图像的配准。实验结果表明，本文方法在保证遥感图像配准精度的同时，配准速度相比于传统配准方法得到较大提升。

Abstract: In this paper, because the traditional registration algorithms for remote sensing image are slower and don’t meet the requirements of real-time problem, a new method is proposed based on the combination of improved AGAST and FREAK for fast remote sensing images registration. Firstly, the improved AGAST is used to detect the feature points between reference image and image that is to be registered; Secondly, FREAK algorithm is used to obtain a binary string descriptor, and hamming distance between features vector is computed by using a cascade match to get matching feature points; Finally, wrong match pairs are eliminated by using the improved similar triangle method, and the optimal spatial geometric transform parameters are estimated using the least square method to accomplish the two images registration. Experimental results show that the pro- posed method improves the registration rate compared to the traditional registration methods, and ensures accuracy at the same time.