结合相位对称与基于排序LSS的多模态遥感影像匹配

Multimodal Remote Sensing Image Matching Combining Phase Symmetry and Rank-based LSS

针对多模态遥感影像存在非线性辐射畸变的问题,本文提出了一种结合相位对称特征与基于排序局部自相似性的多模态遥感影像匹配方法.首先,利用影像的局部相位信息构造相位对称图,在此基础上利用加速分段测试特征提取算法(features from accelerated segment test,FAST)对相位对称图进行特征提取.然后结合基于排序的局部自相似性与相位一致性构造一种新的特征描述符RPCLSS(combining rank,phase congruency and local self-similarity descriptor).最后利用快速抽样一致性算法(fast sample consensus,FSC)进行误匹配点剔除.将本文方法在公开的多源遥感影像数据集上与现有的5种先进匹配方法进行对比实验.实验结果表明,本文方法在正确匹配点数量、匹配精度和匹配正确率方面,优于现有的先进多模态遥感影像匹配方法.

To address the issue of nonlinear radial distortion present in multimodal remote sensing images,this study proposes a method for matching multimodal remote sensing images that integrates phase symmetry features with rankbased local self-similarity.Initially,the local phase information of the images is utilized to construct a phase symmetry map,upon which feature extraction is performed using the features from the accelerated segment test(FAST)algorithm.Subsequently,a new feature descriptor named RPCLSS is constructed,which combines rank-based local self-similarity and phase congruency.Finally,the fast sample consensus(FSC)algorithm is employed to eliminate mismatched points.Comparative experiments are conducted on publicly available multi-source remote sensing image datasets,comparing the proposed method against five existing advanced matching methods.The results reveal that the proposed method outperforms these state-of-the-art methods in terms of the number of correct matching points,matching precision,and matching correctness.