A Registration Method for Historical Maps Based on Self-Supervised Feature Matching

Comparing historical map images of the same region from different periods is an effective method for studying urban history and planning. Image registration techniques in the field of computer vision can be applied to this task. However, historical map registration faces unique challenges, including insufficient training data, variations in image sizes, and unavailable texture features. To address these challenges, we constructed a dedicated dataset of over 100 scanned historical maps, including both raw and preprocessed segmented images. We then developed an enhanced SuperGlue-based registration framework, optimized for the specific obstacles posed by historical maps, such as low texture and large image size. Additionally, we proposed a self-supervised fine-tuning feature extraction algorithm and a Transformer-based architecture utilizing graph attention mechanisms to refine feature descriptors and enhance feature matching performance. Experimental results indicate that our solution achieves superior performance compared to existing models, with RMSE reduced by up to 20%, ROCC improved by up to 10%, and processing time shortened by at least 15%.