基于sMLD特征的远红外航空图像配准算法

Registration method of far infrared aerial images based on sMLD feature

航空场景下的远红外图像迫切需要准确、鲁棒、快速的特征描述与自动配准方法。由于已有的多重线型描述符MLD特征存在“特征孤岛”和“尺度变换受限”的问题,因此针对远红外航空图像的配准问题,文章提出了一种聚合特征点以及线型描述符分段统计的特征描述方式,即sMLD特征。结合sMLD特征相互连接构成网状拓扑结构的特性,还提出了一种由粗到细的分支加速匹配算法RF-BA。其中,RF-BA粗匹配充分利用拓扑图的结构,通过局部寻优算法提高匹配的效率。RF-BA精匹配利用最小外接凸四边形原则和GMS校验原则,提升配准精度。实验结果表明,与已有的几种代表性配准方法相比,所提方法在配准精度和时间开销方面均具有更好的性能。

Accurate,robust,and fast feature representation and automatic registration are urgent needs for far infrared image in aerial scenes.Since the existing Multiple Line Descriptors(MLD)have the problems of“isolated feature”and“limited scale transformation”,thus a feature description method that combines feature points and line descriptors partition statistics is proposed.This paper refers to the feature descriptor as the Segmented MLD(sMLD).Combining the characteristics that sMLD feature connect with each other to form a mesh topology structure,a coarse-to-fine branch accelerated matching(RF-BA)algorithm is also proposed.The RF-BA coarse matching improves the matching efficiency by making full use of the topology structure and local optimal algorithm.The RF-BA fine matching improves the registration accuracy by using the minimum circumscribed convex quadrilateral principle and GMS verification principle.Experimental results show that compared with other existing registration methods,the method has better performance in terms of registration accuracy and running time.