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A study on the classification of vegetation point cloud based on random forest in the straw checkerboard barriers area
- Source :
- Journal of Intelligent & Fuzzy Systems. 41:4337-4349
- Publication Year :
- 2021
- Publisher :
- IOS Press, 2021.
-
Abstract
- Aiming at the problem of automatic classification of point cloud in the investigation of vegetation resources in the straw checkerboard barriers region, an improved random forest point cloud classification algorithm was proposed. According to the problems of decision tree redundancy and absolute majority voting in the existing random forest algorithm, first the similarity of the decision tree was calculated based on the tree edit distance, further clustered reduction based on the maximum and minimum distance algorithm, and then introduced classification accuracy of decision tree to construct weight matrix to implement weighted voting at the voting stage. Before random forest classification, based on the characteristics of point cloud data, a total of 20 point cloud single-point features and multi-point statistical features were selected to participate in point cloud classification, based on the point cloud data spatial distribution characteristics, three different scales for selecting point cloud neighborhoods were set based on the point cloud density, point cloud classification feature sets at different scales were constructed, optimizing important features of point cloud to participate in point cloud classification calculation after variable importance scored. The experimental results showed that the point cloud classification based on the optimized random forest algorithm in this paper achieved a total classification accuracy of 94.15% in dataset 1 acquired by lidar, the overall accuracy of classification on dataset 2 obtained by dense matching reaches 92.03%, both were higher than the unoptimized random forest algorithm and MRF, SVM point cloud classification method, and dimensionality reduction through feature optimization can greatly improve the efficiency of the algorithm.
- Subjects :
- Statistics and Probability
Hydrology
010504 meteorology & atmospheric sciences
Computer science
0211 other engineering and technologies
General Engineering
Point cloud
02 engineering and technology
Straw
01 natural sciences
Random forest
Artificial Intelligence
Checkerboard
medicine
medicine.symptom
Vegetation (pathology)
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Subjects
Details
- ISSN :
- 18758967 and 10641246
- Volume :
- 41
- Database :
- OpenAIRE
- Journal :
- Journal of Intelligent & Fuzzy Systems
- Accession number :
- edsair.doi...........38e2988e3e4c721828086fe57bdb5eaa