Successful remote sensing image registration is one of the foundations of many remote sensing applications. Image high-lever features extracted by convolutional neural network (CNN) have achieved excellent performance in image classification and retrieval, and can be used to solve some problems of low-lever image registration features, such as the limitation of expression capability and easily being interfered. Hence, in this paper, the authors investigated the problem as to how to use CNN feature for remote sensing image registration. First, the authors investigated different CNN features from fully connected layers and aggregating convolutional features with different sizes from convolutional layer to register remote sensing image. Then the authors introduced the procedure by using CNN feature for image registration. Finally, the authors compared the registration performances of CNN features and scale-invariant feature transform (SIFT) features after the transformation of the image’s perspective, brightness and scale, respectively. The experimental results show that the CNN feature has better matching performance than the SIFT method in terms of matching accuracy and correct number of corresponding points. The finely tuned CNN feature has stronger robustness to the transformed image than the SIFT feature.
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