A new method for the extraction of shoreline based on point cloud distribution characteristics

Chao Lv; Weihua Li; Jianglin Liu; Jiuming Li

doi:10.1117/12.3014390

9 January 2024 A new method for the extraction of shoreline based on point cloud distribution characteristics

Chao Lv, Weihua Li, Jianglin Liu, Jiuming Li

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129692L (2024) https://doi.org/10.1117/12.3014390
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

The commonly used shoreline extraction methods need to go through the generation process of point cloud digital elevation model (DEM) with large amount of calculation and easy to introduce errors. This proposed method used the change law of the coordinate value of the point at the time of obtaining the data due to the consistent acquisition method, puts forward the algorithm, and improves the accuracy and efficiency of the algorithm, so that it can quickly and accurately extract the boundary point cloud of the coastal zone, properly process the extracted boundary point cloud and transform it into the coastline point cloud in the strict sense. Compared with the measured coastline data and the coastline data extracted by isoline tracking method, the visual and quantitative data show that the extraction effect of this method is more continuous and the accuracy is higher. It can be seen from the data table that the overall standard deviation and variance of coastline extracted by this method are reduced from 0.3726 and 0.1415 of isoline tracking method to 0.1632 and 0.0266 respectively

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chao Lv, Weihua Li, Jianglin Liu, and Jiuming Li "A new method for the extraction of shoreline based on point cloud distribution characteristics", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129692L (9 January 2024); https://doi.org/10.1117/12.3014390

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