The invention discloses a dual-source image feature extraction and fusion identification method based on a convolution neural network, which comprises the following steps: utilizing the characteristics of the convolution neural network with migration learning, training the convolution neural network model parameters through a large number of visible light databases; The trained model is used to automatically extract the hidden features of visible and thermal infrared target images, and the maximum desampling method is used to reduce the feature dimension. Combining Fisher discriminant method and principal component analysis algorithm, the dimension reduction and fusion of multi-source image features are carried out. Support vector machine classifier is used to classify and recognize the fusion features of the target image. A method for classify and identifying multi-source image target in unmanned aerial vehicle (UAV) platform features that image hidden features are extracted by convolution neural network, and Fisher discriminant method and principal component analysis algorithm are combined for dimension reduction and fusion of features, which provides a new and effective way forclassifying and identifying multi-source image targets based on feature level.
Feature extraction and fusion recognition of dual-source images based on convolution neural network