Heterogeneous remote-sensing image matching method based on deep learning
Huitai Hou, Qing Quan Xu, Chaozhen Lan, Zhixiang Cui, Jianqi Qin, Lianxia Wang · Journal of Physics Conference Series · 2020
Abstract In this paper, a deep learning matching method is proposed to address the difficulty in matching heterogeneous remote sensing images, which is caused by their differences in imaging modes, time phases, and resolutions. A heterogeneous image is inputted into the convolutional neural network (CNN) to extract deep features and, then, a graph neural network (GNN) is used for matching. Finally, correct matching points are retained while ensuring the effective elimination of mismatches. The algorithm adopted in this study was tested using multiple sets of heterogeneous remote-sensing images and compared with D2-Net+NN+RANSAC and Superpoint+SuperGlue algorithms. The results show that the algorithm used in this study possesses strong adaptability and robustness and is an optimal algorithm for the robust matching of remote-sensing images with different sources and large differences.