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QIN Jianqi, LAN Chaozhen, CUI Zhixiang, ZHANG Yongxian, WANG Yan. A Reference Satellite Image Retrieval Method for Drone Absolute Positioning[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200229
Citation: QIN Jianqi, LAN Chaozhen, CUI Zhixiang, ZHANG Yongxian, WANG Yan. A Reference Satellite Image Retrieval Method for Drone Absolute Positioning[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200229

A Reference Satellite Image Retrieval Method for Drone Absolute Positioning

doi: 10.13203/j.whugis20200229
Funds:

Leading Talents Fund in Science and Technology Innovation in Henan Province (194200510023)

  • Received Date: 2020-05-09
    Available Online: 2021-05-07
  • Focusing on the problem of global absolute positioning of drones under global navigation satellite system (GNSS) denied environment, a reference satellite image retrieval method that aggregates deep learning features is proposed. First, the pre-trained deep learning model is used to extract local convolution features of the drone images and satellite images. Then, the local aggregation descriptor vector is used to generate the global expression of the images. Finally, the global feature of the image is used to perform similarity retrieval and the post-processing method of matching precisely and reranking the retrieval results is used, which further improves the retrieval accuracy. A new satellite reference image data set for absolute positioning of drone is designed and tested. The results show that the accuracy of the method used to retrieve the satellite reference image in the drone image adaptation area is 76.07%, which can provide a reference for the subsequent absolute positioning of vision-based drones.
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A Reference Satellite Image Retrieval Method for Drone Absolute Positioning

doi: 10.13203/j.whugis20200229
Funds:

Leading Talents Fund in Science and Technology Innovation in Henan Province (194200510023)

Abstract: Focusing on the problem of global absolute positioning of drones under global navigation satellite system (GNSS) denied environment, a reference satellite image retrieval method that aggregates deep learning features is proposed. First, the pre-trained deep learning model is used to extract local convolution features of the drone images and satellite images. Then, the local aggregation descriptor vector is used to generate the global expression of the images. Finally, the global feature of the image is used to perform similarity retrieval and the post-processing method of matching precisely and reranking the retrieval results is used, which further improves the retrieval accuracy. A new satellite reference image data set for absolute positioning of drone is designed and tested. The results show that the accuracy of the method used to retrieve the satellite reference image in the drone image adaptation area is 76.07%, which can provide a reference for the subsequent absolute positioning of vision-based drones.

QIN Jianqi, LAN Chaozhen, CUI Zhixiang, ZHANG Yongxian, WANG Yan. A Reference Satellite Image Retrieval Method for Drone Absolute Positioning[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200229
Citation: QIN Jianqi, LAN Chaozhen, CUI Zhixiang, ZHANG Yongxian, WANG Yan. A Reference Satellite Image Retrieval Method for Drone Absolute Positioning[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200229
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