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10 November 2021 Classification of terresrial laser scanning data based on multi-dimensional geometry features
Xinyi Zhang, Maolin Chen, Chunsen Tan, Hong Ma
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Proceedings Volume 12050, International Conference on Smart Transportation and City Engineering 2021; 120505B (2021) https://doi.org/10.1117/12.2613698
Event: 2021 International Conference on Smart Transportation and City Engineering, 2021, Chongqing, China
Abstract
Terrestrial laser scanning (TLS) data collected in urban scenes contain variable point densities, bringing great challenges for automatic extraction of urban objects. Aiming at the problem of the density variation of large-scale TLS data, this paper improves density-adaptive feature, and the number of projection points is replaced by relative density feature. The vertical slice feature is added into our method to form a 20-dimensional feature vector together with traditional 3D features and 2D features. Random Forest is used to identify the class of each point and evaluate the importance of each feature type. The experimental results show that the feature extraction algorithm in this paper can perform semantic segmentation on large-scale TLS data in urban scene more accurately than commonly used feature combinations, especially in man-made terrain, low vegetation, buildings, hard scape and scanning artifact classes, with an overall accuracy of 97.35%.
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Xinyi Zhang, Maolin Chen, Chunsen Tan, and Hong Ma "Classification of terresrial laser scanning data based on multi-dimensional geometry features", Proc. SPIE 12050, International Conference on Smart Transportation and City Engineering 2021, 120505B (10 November 2021); https://doi.org/10.1117/12.2613698
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KEYWORDS
Laser scanners
Feature extraction
Scene classification
Laser classification
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