Fast retrieval method of remote sensing image for UAV absolute location

Focusing on the absolute positioning problem of UAV scene matching visual navigation in complex environment, a fast real-time image retrieval method based on aggregation of deep learning features is proposed. Firstly, NetVLAD, a trainable soft assignment deep learning framework, is introduced to extract and aggregate the image stable global feature representation vector with VGG16 network. Secondly, in the initial retrieval stage, KD tree structure is utilized to construct the retrieval index of image global feature vector, which can improve the retrieval speed without losing the retrieval accuracy. Finally, the initial retrieval results are judged quickly by using the Pearson product-moment correlation coefficient that can automatically filter the initial retrieval results. Graph neural network SuperGlue, a feature learning and matching algorithm is utilized to match and reorder the images that need to be reordered. The proposed method is tested by grouping open summer and winter remote sensing image datasets. The experimental results show that under the condition of no reordering, the average accuracy of the first image of the initial retrieval results reaches 58.27%, and the accuracy of some areas with better features reaches 85%. It also has good adaptability to remote sensing images of different time phases and takes 3.7 s on average to retrieve an image, which can provide reference for UAV scene matching navigation initial positioning. © 2024 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved.