Abstract
With the continuous advancement of science and technology, the improvement of mobile terminal hardware performance and the large-scale popularization of smart phones have brought new experiences and methods to surveying and mapping work. This article mainly studies the application of remote sensing image processing technology based on mobile augmented reality technology in surveying and mapping engineering. First, perform grayscale processing on the image in the experiment, then remove the noise in the image and smooth the image through the median filter method and finally use the Canny operator to perform edge detection to obtain a binarized image containing only the target object, and this is done by image feature extraction. After using three-dimensional scanning modeling to extract the image feature points, the target manager is used for sample analysis. Obtain the projection matrix through the interface, and then perform coordinate conversion to complete the positioning of the target scene. In this paper, the BRISK feature point detection algorithm with fast speed and small calculation is used to detect the target, and SVM is used for remote sensing feature classification. Experimental data show that the recognition success rate of the algorithm is 84%. The results show that mobile augmented reality technology and remote sensing image processing technology can improve the efficiency and accuracy of surveying and mapping engineering, and have strong ease of use and stability.
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04 November 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s00500-024-10298-w
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Acknowledgements
This work was supported by National Natural Science Foundation of China. (Research on theory and technology of Intelligent self-organizing Network for large-scale UAV cluster, No.61931011)
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Lu, W., Zhao, L. & Xu, R. RETRACTED ARTICLE: Remote sensing image processing technology based on mobile augmented reality technology in surveying and mapping engineering. Soft Comput 27, 423–433 (2023). https://doi.org/10.1007/s00500-021-05650-3
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DOI: https://doi.org/10.1007/s00500-021-05650-3