Amalgamating Convolutional and Graph Neural Networks for Fast Multimodal Remote Sensing Image Registration

Multimodal remote sensing image registration is crucial for the comprehensive utilization of multimodal data. Existing methods demonstrate limited robustness in addressing significant nonlinear radiometric differences and geometric distortions in multimodal remote sensing images. This paper presents ACGNet, a fast registration method that amalgamates convolutional and graph neural networks to address these challenges. ACGNet first uses a deep convolutional network with a dual-branch encoder-decoder architecture to simultaneously extract feature points and corresponding advanced descriptors. Subsequently, a graph neural network based on attention mechanisms is employed for feature matching and outlier rejection, aiming to obtain a large number of high-confidence correspondences, which are crucial for estimating transformation parameters. Experimental results demonstrate that the proposed method exhibits robustness to variations in scale and rotation, and can efficiently and stably perform multimodal remote sensing image registration, outperforming other methods in terms of speed and accuracy.