A spatial multi-scale integer coding method and its application to three-dimensional model organization

Lai, Guangling; Tong, Xiaochong; Zhang, Yongsheng; Ding, Lei; Sui, Yinling; Lei, Yi; Zhang, Yong

doi:10.1080/17538947.2019.1690594

Cited by 7 publications

(1 citation statement)

References 45 publications

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“…To globally store the information on control points, this study uses the global discrete grid system to organize the landmark control point library and adopts the strategy of dividing the globe into regular grids and establishes a corresponding relationship between the control points and grids via coding. The location coding information is mainly used to realize the fast indexing of landmarks [33], [34]. When a satellite with a poor positioning precision has to use control point data, landmark detection is first performed on the satellite image.…”

Section: B Landmark Control Point Metadata Systemmentioning

confidence: 99%

Method for the Automatic Generation and Application of Landmark Control Point Library

et al. 2020

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Ground control points play an important role in improving the positioning accuracy of satellite images. At present, most control points must be obtained by manual deployment (calibration field) or feature extraction. The control points obtained by manual deployment are fixed in certain areas and have a high deployment cost. The current method for feature matching is limited by the acquisition of the reference image and matching accuracy, which results in poor flexibility and is not conducive to improvements to worldwide satellite positioning precision. To solve this problem, this study proposes a new automatic generation and application algorithm for landmark control points across the globe based on the deep learning method. Using this method, landmarks can be selected and the deep-learning-based target-detection method can be used to realize the automatic generation of control points. When satellite images with relatively low positioning precision are used, landmark control points can be accurately obtained with a precision reaching the sub-pixel level, which can provide a sufficient foundation for the geometric correction of non-mapping satellite images. In this study, a remote sensing image dataset of road intersections was also constructed, which considers road intersections as landmarks. The experiment is carried out with the road intersection dataset, and CenterNet network is trained. The experiments show that the detection precision of the network can reach 96.27%. Finally, we designed an application strategy for the landmark control points and improved the image matching method, such that the matching precision between the landmark images and images to be processed can reach the sub-pixel level and conform to the requirements of geometric correction for non-mapping satellite images.

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Section: B Landmark Control Point Metadata Systemmentioning

confidence: 99%