摘要
针对SAR图像配准中匹配效率低、误匹配对多和配准精度差的问题,提出一种基于局部不变特征的SAR图像配准新算法.首先,使用加速分割检测特征(features from accelerated segment test,FAST)检测算法,检测SAR图像的FAST角点;使用DAISY描述子对FAST特征进行描述,得到SAR图像不变特征。其次,采用基于KD树的欧氏距离匹配策略,实现特征点对的粗匹配;采用RANSAC算法去除误匹配,实现特征点对精匹配.然后,采用仿射变换模型,实现图像插值和图像变换,实现SAR图像粗配准。最后,建立配准精度评估反馈机制,实现配准优化.通过使用不同时相、不同工作模式HJ-1C星载SAR和不同极化、不同波段机载AIRSAR图像配准实验,提出算法与经典不变特征配准算法相比,具有适配性好、配准效率高的优点.
Aiming at the problems of low performance matching, more mismatching pairwise, and low registration precision, which are the characteristic of traditional SAR image registration methods, we propose a novel and efficient local invariant feature-based algorithm. First, the feature points are detected by features from accelerated segment test（ FAST） method and described by DAISY descriptor in SAR image. Second, Kd-tree-based dual-matching strategy and random sample consensus （ RANSAC ） are used to establish fine feature matching. Third, affine transform model is estimated for image resampling and transformation, and rough registration is implemented. Finally, feedback mechanism is constituted for fine registration based on the estimation of registration precision. The flexibility and efficiency is demonstrated by experiments with slant range SAR images acquired from different working model, different times, viewpoints, wavelengths and polarizations.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2014年第11期112-118,共7页
Journal of Harbin Institute of Technology
