Multi-Source Remote Sensing Image Registration Based on Local Deep Learning Feature
Yongxian Zhang, Zhijun Zhang, Guorui Ma, Jiao Wu · 2021
Due to huge differences in radiation characteristics and geometric characteristics of multi-source remote sensing images, presenting a big challenge for high-precision registration. In this paper, a new registration method based on deep learning is proposed. First, we use the convolutional neural network to extract deep learning features of the reference and sensed image after adaptive down-sampling, and extract 512-dimensional descriptor on the feature map to calculate the matching result, homography matrix and overlap area. Then, the circumscribed rectangle of the overlapping area is divided into blocks, and the same name point information extracted from all sub-blocks is combined to obtain matching result of source image pair, and then homography matrix of the source image pair is estimated. Finally, the registration result is obtained. Results show that the proposed algorithm has strong adaptability and robustness in the registration of multiple heterogeneous images in mountains, hills and plains.