Experimental design of decision-level fusion block filtering based on ground imagery and LiDAR point cloud data

Jinyu Liu; Shoujun Li; Donghai Mao; Bolin Chu

doi:10.1117/12.3032147

3 June 2024 Experimental design of decision-level fusion block filtering based on ground imagery and LiDAR point cloud data

Jinyu Liu, Shoujun Li, Donghai Mao, Bolin Chu

Author Affiliations +

Proceedings Volume 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024); 1317016 (2024) https://doi.org/10.1117/12.3032147
Event: Third International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 2024, Wuhan, China

Abstract

With the purpose of better adapt to different types of complex terrain, this paper proposes an experimental scheme for ground image and LiDAR point cloud decision-level fusion block filtering and DEM generation. Firstly, the ground image and point cloud are matched through homologous feature points linear transformation to obtain more spectral texture information from the image. Then, based on decision-level fusion, the original point cloud is segmented into several independent blocks. The IPTD filtering algorithm is improved and optimized by using a search method according to the different multi-dimensional detail features of each block region. Finally, the filtered total ground points are interpolated to obtain a DEM model. The results show that compared with other overall filtering schemes, the DEM generated by this algorithm has the highest accuracy with an average absolute error and root mean square error of 0.100 meters and 0.157 meters respectively, greatly optimizing the generation effect of DEM.

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

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Jinyu Liu, Shoujun Li, Donghai Mao, and Bolin Chu "Experimental design of decision-level fusion block filtering based on ground imagery and LiDAR point cloud data", Proc. SPIE 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 1317016 (3 June 2024); https://doi.org/10.1117/12.3032147

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KEYWORDS

Tunable filters

Point clouds

Image fusion

Optical filters

Image filtering

Image segmentation

Data fusion

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