ABSTRACT
When classifying objects in 3D LiDAR data, it is important to use efficient collection methods and processing algorithms. This paper considers the resolution needed to classify 3D objects accurately and discusses how this resolution is accomplished for the RedTail RTL-450 LiDAR System. We employ VoxNet, a convolutional neural network, to classify the 3D data and test the accuracy using different data resolution levels. The results show that for our data set, if the neural network is trained using higher resolution data, then the accuracy of the classification is above 97%, even for the very sparse testing set (10% of original test data set point density). When the training is done on lower resolution data sets, the classification accuracy remains good but drops off at around 3% of the original test data set point density. These results have implications for determining flight altitude and speed for an unmanned aerial vehicle (UAV) to achieve high accuracy classification. The findings point to the value of high-resolution point clouds for both the training of the convolutional neural network and in data collected from a LiDAR sensor.
