基于典型相关分析的遥感影像变化检测

多通道遥感影像由于通道之间相关性的影响,相对于单通道影像的变化检测更为困难,因此需要有效的集中分布在各个通道上的变化信息,构造出不同时相之间的差异影像,以便于变化信息的分析解译。针对多通道变化信息集中的难点和通道之间相关性的影响难以消除的问题,引入多元统计分析中的典型相关分析方法,将2个时相的多通道遥感影像示作2组多元随机变量,采用多元变化检测变换,对多个波谱通道上的所有差异信息或变化信息进行重组,分配到一组互不相关的结果变量中,最大限度地消除通道间的相关性对变化检测的不利影响,初步解决了差异影像构造的问题。

Detection of remote sensing image alteration based on canonical correlation analysis

Because of the effect of correlativity between channels,it is more difficult to detect the change of multi-channel remote sense images than to defect the change of single-channel images.Therefore effective concentration of the changing information on each channel is required to form difference images between different time phases so as to facilitate the analysis and interpretation of changing information.The problem is that it is difficult to concentrate multi-channel changing informations and eliminate the effect of correlativity between multi-channel remote sense images.With regard to this problem,the canonical correlation analysis is introduced in multivariate statistical analysis.The authors take the multi-channel remote sense images in two time phases as two sets of multivariate random variables and adopt multivariate alteration detection to reorganize all difference information or changing information in spectral passages and distribute them in a group of uncorrelated variables.Thus the adverse effect of the correlativity between channels on the multivariate alteration detection is minimized and the problem of difference image structure is solved preliminarily.