Intelligent Matching Method for Heterogeneous Remote Sensing Images Based on Style Transfer
Jiawei Zhao, Dongfang Yang, Yongfei Li, Peng Xiao, Jinglan Yang
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>.