Intelligent Matching Method for Heterogeneous Remote Sensing Images Based on Style Transfer

Abstract

Intelligent matching of heterogeneous remote sensing images is a common basic problem in the field of intelligent remote sensing image processing. Aiming at the difficulty of matching satellite-aerial remote sensing images, this paper proposes an intelligent matching method for heterogeneous remote sensing images based on style transfer. First, based on the idea of image style transfer of a generative adversarial networks, this method improves the conversion effect of the model on heterogeneous images by constructing a new generative network loss function and converts satellite images into aerial images. Then, the advanced deep learning-based matching Algorithms D2-Net and LoFTR are used to achieve matching between the generated aerial image and the original aerial image. Finally, this transformation relationship is mapped to the corresponding satellite-aerial image pair to obtain the final matching result. The image style transfer experiments and the matching experiments we carry out under different test datasets show that the smooth cycle-consistent generative adversarial networks proposed in this paper can effectively reduce the complexity of the algorithm and improve the quality of image generation. In addition, combining it with deep learning-based feature matching methods can effectively improve the accuracy and robustness of the matching algorithm. Our code and data can be found at: <uri>https://gitee.com/AZQZ/intelligent-matching</uri>.