Feature-level Fusion of Infrared and Visible Images Based on Principal Component Analysis plus Canonical Correlation Analysis

A new fusion algorithm based on PCA(Principal Component Analysis) and CCA(Canonical Correlation Analysis) is proposed according to feature-level fusion of infrared and visible images.The features of infrared and visible images are abstracted respectively.When the feature dimension is higher,objective function based on the CCA method will face the problem of singular covariance matrix and can not be solved.PCA method is firstly used to reduce the dimension,and then CCA method is adopted to solve the fusion feature in the low-dimensional space.Simulation results show that the proposed algorithm could effectively extract the fusion feature,and the recognition effect is higher than single CCA identification method.