Improvement of LiDAR data classification algorithm using the machine learning technique
Improvement of LiDAR data classification algorithm using the machine learning technique
复制标题
利用机器学习技术改进LiDAR数据分类算法
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Songxin Tan
中科院分区：
文献类型：
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作者：
Ali Haider;Songxin Tan
激光雷达测量的研究在包括森林遥感在内的各种应用中具有重要意义。其中，偏振激光雷达是一种较新的重要主动遥感工具。本研究对偏振激光雷达和非偏振激光雷达的系统性能进行了全面的描述。值得注意的是，两种激光雷达系统的相对性能可以利用人工神经网络、k-NN （k-最近邻）分类器和判别函数等几种分类器来估计。在每种态度中，主要的方面是比较不同分类器获得的分类结果，以获得改进的激光雷达性能。在这种情况下，使用随机选择的一组树木，如松树、榆树、蓝云杉、枫、樱桃和绿灰，测试了极化和非极化波形特征用于分类的效用。k-NN分类器使用非偏振数据获得92%的准确率。然而，对于k- nn分类器，k的值由用户提供。引人注目的是，同样的k-NN分类器使用偏振数据达到了96%的准确率。同样，人工神经网络分类器使用偏振激光雷达数据获得了96%的分类精度，而使用非偏振激光雷达数据获得的分类精度约为89%。大多数较差的表现是通过判别分析得到的。在非极化数据的情况下，判别分析结果只有59%的效率。相比之下，使用偏振数据进行判别分析的分类准确率约为75%。虽然列表分类器使用极化数据比使用非极化数据表现更好，但人工神经网络可以获得更好的性能。
Study of the lidar measurement is very significant in a variety of applications including forest remote sensing. Among them, polarimetric lidar is a relatively new but important active remote sensing tool. This study covers a comprehensive description of the system performance of both the polarimetric lidar and non-polarimetric lidar. Noticeably, relative performances of both lidar systems can be estimated exploiting several classifiers such as artificial neural network, k-NN (k-nearest neighbor) classifier and the discriminant function. In each of these attitudes, the principal aspect is to compare the classification results obtained by different classifiers to obtain improved lidar performance. In this case, utility of polarimetric and non-polarimetric waveform features for classification was tested using a group of randomly selected trees such as pines, elm, blue spruce, maple, choke cherry, and green ash. The k-NN classifier obtained 92% accuracy using non-polarimetric data. However, for k-NN classifier the value of k is provided by the user. Strikingly, same k-NN classifier achieved 96 % accuracy using polarimetric data. Again, artificial neural network classifier achieved 96% classification accuracy using polarimetric lidar data whereas the classification accuracy it received was around 89 % using non-polarimetric lidar data. Most poor performance was received by discriminant analysis. In case of nonpolarimetric data, discriminant analysis results in only 59 % efficiency. In contrast, about 75 % classification accuracy was observed using polarimetric data for discriminant analysis. Though the listed classifiers can perform better using polarimetric data than non-polarimetric data, artificial neural network can be employed for better performance.