Hughes, L.H., Schmitt, M., Mou, L., Wang, Y. and Zhu, X.X. (2018) Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN. IEEE Geoscience and Remote Sensing Letters, 15, 784-788.
https://doi.org/10.1109/LGRS.2018.2799232

has been cited by the following article:

• TITLE: Research on Vector Road Data Matching Method Based on Deep Learning

  AUTHORS: Lin Zhao, Yanru Liu, Yuefeng Lu, Ying Sun, Jing Li, Kaizhong Yao

  KEYWORDS: Deep Learning, Vector Matching, Similarity, VGG16, Siamese Network

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.11 No.1, January 31, 2023

  ABSTRACT: Most of the existing vector data matching methods use traditional feature geometry attribute features to match, however, many of the similarity indicators are not suitable for cross-scale data, resulting in less accuracy in identifying objects. In order to solve this problem effectively, a deep learning model for vector road data matching is proposed based on siamese neural network and VGG16 convolutional neural network, and matching experiments are carried out. Experimental results show that the proposed vector road data matching model can achieve an accuracy of more than 90% under certain data support and threshold conditions.