Mr. Chao Tang Profile

Chao Tang

at Beijing Urban Construction Exploration and Surveying Design Resea

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Proceedings Article | 5 November 2020 Paper

Studies on automatic extraction of precise 3D laser measurement data in specific section of subway tunnel

Chao Tang, Haiqian Hou, Tingli Fan, Lifeng Zhao, Yong Wang, Zihao Li, Xiaojing Wang

Proceedings Volume 11562, 1156212 (2020) https://doi.org/10.1117/12.2580108

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In the construction period and operation period of an urban subway, due to the comprehensive impact of geology, groundwater, the construction of adjacent foundation pits and the load of its own structure, the tunnel structure of the subway may go through deformation and other changes that endanger the safety of the tunnel. With the growing scales of subways and tunnels projects, structure monitoring becomes increasingly important and a fast and accurate extraction method is critical for the monitoring of tunnel structures. In this paper, a method of extracting tunnel section based on gray map is proposed to fit the tunnel section, which is applied in a subway tunnel interval in Suzhou city. Data such as horizontal convergence value and ellipticity of a particular section in the test interval are extracted, and the results are compared with those measured by the total station measurement method at the same time. The results show that the proposed method can extract a specific section accurately and takes a short computing time. Compared to the traditional total station measurement method, the 3D laser measuring method not only improves the efficiency and data comparability of the tunnel deformation in the long-term operation monitoring, but also makes it easier for structured data to be used in the evaluation of the tunnel operation status and the development trend of the long-term deformation of a tunnel.

Proceedings Article | 24 October 2017 Paper

An algorithm for object recognition in hyperspectral remote sensing images and its application to lithologic feature extraction

Yu Liu, Chao Tang, Guanghui Wang, Xinyuan Gao

Proceedings Volume 10462, 104624L (2017) https://doi.org/10.1117/12.2285564

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This study, aimed at the problems of spectrum waveform characteristic distinction, operation speed, and spatial detail, proposes an improvement in the algorithm for hyperspectral remote sensing feature recognition. Based on this, we propose a fractal signal algorithm. The performance, efficiency, etc., of the algorithm itself is tested using CASI hyperspectral data and hyperspectral remote sensing image lithologic characteristics of the study area are also extracted. The initial value of the signal, the iteration step length, and other characteristics of the fractal signal in hyperspectral remote sensing data were discarded in this study. To a certain extent, the fractal signal algorithm can refine the distinguishability of similar characteristics in hyperspectral, and when used for feature extraction from CASI lithology data it accurately extracted the surface lithology of exposed bedrock areas.

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