An automatic extraction method of image control points from CORS high-precision data

doi:10.13474/j.cnki.11-2246.2022.0337

Abstract

Abstract: More and more high-precision location points haven been generated by the CORS stations, continuously. How to automatic extract image control points from these high-precision datasets become a huge challenge. It has important value for substituting traditional time-consuming and labor-intensive manual operation or measurement. In this paper, we develop an automatic extraction method of image control points form CORS high-precision data. First, it adopts Douglas-Pucker algorithm to simplify trajectory. Second, ST-DBSCAN is conducted to identify the cluster center. Then, an improved rasterization method is proposed to optimize the candidate of image control points. Finally, we develop an automatic extraction tool based on ArcPy to be deployed in production environment. The experimental results show that our proposed method greatly reduce the workload with the accuracy of 94%, and has a wide field of application with good prospects.

Key words: image control points, CORS, accuracy, trajectory data, data mining, spatio-temporal clustering

CLC Number:

P228

LIANG Lifang, WANG Yanyun, TIAN Shiyu, ZHANG Hengcai. An automatic extraction method of image control points from CORS high-precision data[J]. Bulletin of Surveying and Mapping, 2022, 0(11): 123-127.

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