一种基于特征交汇关键点检测和Sim-CSPNet的SAR图像配准算法

An Algorithm Based on a Feature Interaction-based Keypoint Detector and Sim-CSPNet for SAR Image Registration

合成孔径雷达(SAR)图像存在固有的相干斑噪声和几何畸变,并且其成像过程中图像之间存在非线性辐射差异,因此SAR图像配准是近年来最具挑战性的任务之一。关键点的可重复性和特征描述符的有效性直接影响基于特征的配准方法精度。该文提出了一种新颖的基于特征交汇的关键点检测器,它包含3个并行的检测器,即相位一致性(PC)检测器、水平和垂直方向梯度检测器以及局部变异系数检测器。所提出的特征交汇关键点检测器不仅可以有效提取具有高重复性的关键点,而且大大减少了错误关键点的数量,从而降低了特征描述和匹配的计算成本。同时,该文设计了一种孪生跨阶段部分网络(Sim-CSPNet)来快速提取包含深层和浅层特征的特征描述符。与传统手工设计的浅层描述符相比,它可以用来获得更准确的匹配点对。通过对多组SAR图像进行配准实验,并与其他3种方法进行对比,验证了该方法具有很好的配准结果。

Synthetic Aperture Radar(SAR)image registration has recently been one of the most challenging tasks because of speckle noise,geometric distortion and nonlinear radiation differences between SAR images.The repeatability of keypoints and the effectiveness of feature descriptors directly affect the registration accuracy of feature-based methods.In this paper,we propose a novel Feature Intersection-based(FI)keypoint detector,which contains three parallel detectors,i.e.,a Phase Congruency(PC)detector,horizontal/vertical oriented gradient detectors,and a Local Coefficient of Variation(LCoV)detector.The proposed FI detector can effectively extract keypoints with high repeatabilityand greatly reduce the number of false keypoints,thus greatly reducing the computational cost of feature description and matching.We further propose the Siamese Cross Stage Partial Network(Sim-CSPNet)to rapidly extract feature descriptors containing deep and shallow features,which can obtain more correct matching point pairs than traditional synthetic shallow descriptors.Through the registration experiments on multiple sets of SAR images,the proposed method is verified to have better registration results than the three existing methods.