SPLM-Net:Large Scene SAR Image Registration Based on Point and Line Matching Network

Due to the complex distribution of point features in large-scene synthetic aperture radar (SAR) images, it is challenging to achieve subsequent accurate and robust registration of the images. This paper proposes a SAR point-and-line matching network (SPLM-Net) based on joint matching of sparse key points and line segments. SPLM-Net uses Graph Neural Networks (GNNs) processing to unify points, lines, and their descriptors into a wireframe structure to provide a comprehensive and local discriminant description of the image, and uses dual-softmax mechanism to determine the final feature assignment. SPLM-Net makes up for the shortcoming of high error in point feature matching of large-scale scene images. Experimental results show that the results obtained using this algorithm have significantly improved visual effects and evaluation metrics.