A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier
An increasing number of methods are being used to extract rock discontinuities from 3D point cloud data of rock surfaces. In this paper, a new method for automatic extraction of rock discontinuity based on an improved Naive Bayes classifier is proposed. The method first uses principal component anal...
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/2050 |
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| author | Guangyin Lu Xudong Zhu Bei Cao Yani Li Chuanyi Tao Zicheng Yang |
| author_facet | Guangyin Lu Xudong Zhu Bei Cao Yani Li Chuanyi Tao Zicheng Yang |
| author_sort | Guangyin Lu |
| collection | DOAJ |
| description | An increasing number of methods are being used to extract rock discontinuities from 3D point cloud data of rock surfaces. In this paper, a new method for automatic extraction of rock discontinuity based on an improved Naive Bayes classifier is proposed. The method first uses principal component analysis to find the normal vectors of the points, and then generates a certain number of random point sets around the selected training points for training the classifier. The trained, improved Naive Bayes classifier is based on point normal vectors and is able to automatically remove noise points due to various reasons in conjunction with the knee point algorithm, realizing high-precision extraction of the discontinuity sets. Subsequently, the individual discontinuities are segmented using a hierarchical density-based spatial clustering method with noise application. Finally, the PCA algorithm is used to complete the orientation by plane fitting the individual discontinuities. The method was applied in two cases, Kingston and Colorado, and the reliability and advantages of the new method were verified by comparing the results with those of previous research, and the discussion and analysis determined the optimal values of the relevant parameters in the algorithm. |
| first_indexed | 2024-04-25T00:34:16Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:34:16Z |
| publishDate | 2024-02-01 |
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| spelling | doaj.art-5c318d4277124868bbe6f2194c6852d32024-03-12T16:39:57ZengMDPI AGApplied Sciences2076-34172024-02-01145205010.3390/app14052050A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes ClassifierGuangyin Lu0Xudong Zhu1Bei Cao2Yani Li3Chuanyi Tao4Zicheng Yang5School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaAn increasing number of methods are being used to extract rock discontinuities from 3D point cloud data of rock surfaces. In this paper, a new method for automatic extraction of rock discontinuity based on an improved Naive Bayes classifier is proposed. The method first uses principal component analysis to find the normal vectors of the points, and then generates a certain number of random point sets around the selected training points for training the classifier. The trained, improved Naive Bayes classifier is based on point normal vectors and is able to automatically remove noise points due to various reasons in conjunction with the knee point algorithm, realizing high-precision extraction of the discontinuity sets. Subsequently, the individual discontinuities are segmented using a hierarchical density-based spatial clustering method with noise application. Finally, the PCA algorithm is used to complete the orientation by plane fitting the individual discontinuities. The method was applied in two cases, Kingston and Colorado, and the reliability and advantages of the new method were verified by comparing the results with those of previous research, and the discussion and analysis determined the optimal values of the relevant parameters in the algorithm.https://www.mdpi.com/2076-3417/14/5/2050rock masspoint clouddiscontinuityautomatic extractionmachine learningNaive Bayes classifier |
| spellingShingle | Guangyin Lu Xudong Zhu Bei Cao Yani Li Chuanyi Tao Zicheng Yang A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier Applied Sciences rock mass point cloud discontinuity automatic extraction machine learning Naive Bayes classifier |
| title | A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier |
| title_full | A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier |
| title_fullStr | A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier |
| title_full_unstemmed | A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier |
| title_short | A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier |
| title_sort | new approach for discontinuity extraction based on an improved naive bayes classifier |
| topic | rock mass point cloud discontinuity automatic extraction machine learning Naive Bayes classifier |
| url | https://www.mdpi.com/2076-3417/14/5/2050 |
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