基于联合图频谱特征分析的异源图像匹配方法

Multi-sensor Image Matching Method Based on Joint Graph Spectral Feature Analysis

针对光线强度过高或过低情况下的异源图像匹配问题,提出一种基于联合图频谱特征分析的异源图像匹配方法。首先,采用K近邻法则计算可见光图像与红外图像中角点的结构关系并构建联合图;接着,基于拉普拉斯分解计算联合图中邻接矩阵的特征值从而得到邻接矩阵的特征向量,并通过三维重构构建特征函数对;第三,提出一种基于SUSAN-MSERSURF最大稳定极值区域检测器,检测特征函数对的极值位置;最后,通过对最大稳定极值区域进行归一化后匹配,可以得到异源图像的精确匹配结果。实验结果表明,提出的基于联合图频谱特征分析的匹配方法能够解决光强过高或过低情况下的异源图像匹配问题并取得较优异的匹配率。

Aiming at the problem of multi-sensor image matching when the light intensity is too high or too low,this paper proposes a method of multi-sensor image matching based on joint graph spectrum feature analysis.Firstly,the K-nearest neighbor rule is used to calculate the structural relationship between the corner points in the visible light and infrared images to construct a joint graph.Then,based on Laplacian decomposition,we calculate the eigenvalues of the adjacency matrix in the joint graph to obtain the feature vector of the joint graph,and construct the feature function pair through three-dimensional reconstruction.Thirdly,a maximally stable extremal regions detector based on SUSAN-MSER-SURF is proposed to detect the extreme value position of the feature function pair.Finally,by normalizing the maximum stable extremal regions and matching them,the exact matching results of multi-sensor images can be obtained.The experimental results show that the proposed matching method based on the spectral feature analysis of the joint graph can solve the problem of multi-sensor image matching when the light is too high or too low.