# An InSAR Interference Fringe-Matching Algorithm Based on Mountain Branch Points
## 1 Introduction
Although the ORB algorithm is more efficient than SURF and can process larger images, its matching accuracy is low. In recent years, there have been some related improved algorithms [18; 19; 20]. However, those algorithms all start from feature point detection, which does not take into account the rich terrain information contained in the interference fringes, and when the vehicle flies over a large area of mountains, those algorithms are less effective in matching the texture features between different mountains, due to their similarity. With the advancement of deep learning, its applications in image matching are endless. In 2015, [PERSON] et al. [21] proposed a single sample image recognition method based on twin neural networks. This method  
Figure 1: Framework of the interference fringe-matching navigation system. utilizes two convolutional neural networks with shared parameters to extract image features and calculates similarity using Euclidean distance. Lift [22] can learn the invariance features in the image and convert them into descriptors that can be used for matching and retrieval. In recent years, achievements such as SuperPoint [23] and Superglue[24] have also demonstrated the potential of deep learning in the field of image matching. However, the interference fringe is a special data source, and there is no large public dataset available for training. Models trained on ordinary optical datasets are not effective in the interference fringe matching task. An interferometric fringe registration algorithm has been integrated into common InSAR processing software [25; 26]. This algorithm first simulates a pair of SAR intensity images from DEM data and then uses the simulated images for registration with the real SAR intensity images, thus completing the registration of corresponding interference fringes. However, when an aircraft has attitude errors, these different attitude errors correspond to the same DEM data, with the result that the registration results of interference fringes do not change with the changes in errors. Therefore, it is necessary to develop an effective algorithm that can directly match the interference fringes. However, there is no research on direct matching of interference fringe data, so in this study, we make full use of the topographic information contained in interference fringes and propose a method for matching interference fringes based on mountain branch points. The algorithm was first designed to extract the mountain range line features, detect mountain range branch points on the basis of online features, design feature descriptors in conjunction with mountain range line features and curvatures in the areas around the branch points, and match at Euclidean distances, improving the matching performance of interference fringes compared with conventional algorithms through the use of measured data. The primary focus of this paper is on the matching task of interference fringes, and the research encompasses the following key contributions:  
1. An analysis is conducted of the correspondence between the interference fringes and the DEM to design an algorithm for the extraction of mountain range lines from the interference fringes;
2. The use of mountain range branch points is proposed as a keypoint for feature matching, which is a new idea for image matching of topographic data such as interference fringes and DEM;
3. Improved matching of interference fringes is achieved by designing more specific feature descriptors for mountain branch points.