Dual attention and dual fusion: An accurate way of image-based geo-localization

When GPS signal is interfered or lost, the visual geo-localization method is particularly important for Unmanned Aerial Vehicle (UAV). Since matching UAV images with satellite maps is a multi-source and multi-view problem, visual geo-localization is very challenging. Most existing methods use Convolutional Neural Network (CNN), which extract the final output of the backbone Network to predict the similarity between UAV images and satellite maps. Due to continuous stacked convolution and pooling, rich local information is gradually lost while semantic information is acquired. To solve this problem, a dual attention and dual fusion (DADF) scene matching algorithm is proposed. The contributions of this paper are as follows: 1) In order to achieve accurate matching between UAV and satellite images, a visual geo-localization algorithm based on siamese network is designed. 2) In order to improve the ability of semantic feature extraction, a dual-attention model is constructed. The network pays more attention to the parts that are useful for similarity metric. 3) A dual fusion model is established. According to the feature fusion method and multi-level matching result fusion algorithm, the confidence of matching is improved. To verify the performance of the proposed approach, LA850 and NWPU-ChangAn datasets were collected and enhanced. The experimental results show that the proposed algorithm is more efficient than comparison algorithms.(c) 2022 Published by Elsevier B.V.

Automated summary

In this paper, a dual attention and dual fusion scene matching algorithm is proposed for visual geo-localization of UAV images with satellite maps. The algorithm utilizes a siamese network for accurate matching, a dual-attention model for improved semantic feature extraction, and a dual fusion model for enhanced matching confidence. Experimental results on LA850 and NWPU-ChangAn datasets demonstrate the algorithm's efficiency compared to other methods.