CLASSIFICATION OF AERIAL LASER SCANNING POINT CLOUDS USING MACHINE LEARNING: A COMPARISON BETWEEN RANDOM FOREST AND TENSORFLOW
CLASSIFICATION OF AERIAL LASER SCANNING POINT CLOUDS USING MACHINE LEARNING: A COMPARISON BETWEEN RANDOM FOREST AND TENSORFLOW
复制标题
使用机器学习对航空激光扫描点云进行分类：随机森林和 TensorFlow 之间的比较
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
F. Tonion
中科院分区：
文献类型：
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作者：
F. Pirotti;F. Tonion
抽象的。在这项调查中，两个机器学习（ML）模型的航空激光扫描仪点云的语义分类之间的比较。一个模型是随机森林（RF），另一个是多层神经网络TensorFlow（TF）。在不断增长的训练数据集上比较准确性结果，使用分层独立抽样，从总数据集的5%到50%进行分类。结果表明，RF有平均F1 = 0.823的9类考虑，而TF的平均F1 = 0.450。由于确定TF中神经网络的隐藏层的合适组成的复杂性，RF的F1值高于TF，并且这可能被改进以达到更高的准确度值。计划在这方面进行进一步研究。
Abstract. In this investigation a comparison between two machine learning (ML) models for semantic classification of an aerial laser scanner point cloud is presented. One model is Random Forest (RF), the other is a multi-layer neural network, TensorFlow (TF). Accuracy results were compared over a growing set of training data, using a stratified independent sampling over classes from 5% to 50% of the total dataset. Results show RF to have average F1 = 0.823 for the 9 classes considered, whereas TF had average F1 = 0.450. F1 values where higher for RF than TF, due to complexity in the determination of a suitable composition of the hidden layers of the neural network in TF, and this can likely be improved to reach higher accuracy values. Further study in this sense is planned.