# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 1 Introduction  
Since ancient times, maps have served as records of geographic information, illustrating the spatiotemporal distribution, interconnections, and developmental changes of natural and cultural phenomena. In fields such as historical studies, urban planning, and tourism development, comparing historical map images of the same region from different periods has become an important research paradigm [1, 2, 3]. This is known as _image registration_ from the perspective of computer vision, which spatially aligns images acquired at different times, by different sensors, or from varying viewpoints, enabling accurate positioning, comparison, and fusion among them. An image registration task typically begins with the extraction of a set of representative feature points from a pair of images. Subsequently, these feature points are then matched based on their feature vectors, resulting in a corresponding set of relevant feature points between the two input images. Finally, the transformation model is derived from these matched feature points, describing how one image aligns with the other. Although image registration algorithms have found wide-ranging applications in various computer vision disciplines, including3D reconstruction [4], change detection [5], and medical imaging [6], there has yet to be any specialized research dedicated to the registration of historical map images. Traditional and learning-based methods are the main categories of image registration schemes. Traditional registration approaches are generally divided into intensity-based approaches [7; 8] and feature-based approaches [9; 10]. Although intensity-based methods can produce satisfactory results, they often require substantial human intervention and struggle with images of low contrast. In contrast, feature-based methods, which establish accurate correspondences between images by computing feature points and their corresponding descriptors, are easily affected by noise. Recently, research on learning-based methods has gained popularity and has advanced rapidly [11; 12], providing significant improvements over traditional methods in many aspects. However, their precision tends to drop markedly when dealing with high-resolution images and significant scale differences. In the registration of Synthetic Aperture Radar (SAR) images [13], which resemble historical maps, numerous learning-based methods have also been introduced. Nevertheless, these methods are highly dependent on large volumes of manually annotated training data, making them unsuitable when historical map image data are scarce or difficult to annotate. To address these challenges, we propose a historical map registration framework that focuses specifically on the feature-matching component, which significantly improves overall registration accuracy. 1. We construct a dedicated dataset specifically for historical map registration research. In collaboration with relevant museums and archaeological departments, we collected historical maps spanning various dynasties from cities in China such as Beijing, Tianjin, and Shenyang. These scanned map images were organized into city-based sub-datasets for registration studies. Inspired by the processing of cellular images in medical imaging [14], we designed an improved U-Net-based segmentation model [15] to extract the main contours of the maps from the original images, thereby effectively removing creases, stains, and other artifacts. 2. We propose a new computer vision task focused on historical map registration and outline a corresponding research framework. Drawing inspiration from SAR image registration tasks, which share similarities with map registration, we integrated these approaches with the unique characteristics of historical map data to propose a novel registration architecture. First, we preprocess both the reference image and the moving image to remove noise in the original data. Next, we employ our self-supervised feature extraction method with non-maximum suppression (NMS) [16] to obtain feature points and descriptors.