Conference Proceedings

Classification of Mobile Lidar Data Using Vox-Net and Auxiliary Training Samples

Hanxian He, Kourosh Khoshelham, Clive Fraser

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | The International Society for Photogrammetry, Remote Sensing | Published : 2019

Abstract

The classification of mobile Lidar data is challenged by the complexity of objects in the point clouds and the limited number of available training samples. Incomplete shape, noise points and uneven point density make the extraction of features from point clouds relatively arduous. Additionally, the difference in point density, and size and shape of objects, restricts the utilization of labelled samples from other sources. To solve this problem, we explore the possibility of improving the classification performance of a state-of-the-art deep learning method, Vox-Net, by using auxiliary training samples from a different dataset. We compare the performance of Vox-Net trained with and without t..

View full abstract