A matching method based on improved SuperPoint and linear Transformer for optical and infrared images
A deep learning matching algorithm based on improved SuperPoint and linear transformer was proposed to solve the problem of difficult matching and high mismatching rates between visible and infrared heterologous images.Firstly,based on the SuperPoint network structure,the algorithm introduced the idea of a feature pyramid to build a feature description branch and trained it based on the hinge loss function,so as to better learn the multi-scale deep features of visible and infrared images and increase the similarity of the image with correspondence points to the descriptor.In the feature matching module,SuperGlue was improved by adopting a linear transformer for aggregating features to obtain better matching results.Experiments conducted on multiple datasets demonstrate that the proposed method improves the matching precision and provides better matching performance in comparison with existing matching methods.
万方数据
维普
