RT Journal Article
T1 Disturbance analysis in the classification of objects obtained from urban LiDAR point clouds with convolutional neural networks
A1 Balado Frías, Jesús
A1 Arias Sánchez, Pedro
A1 Lorenzo Cimadevila, Henrique Remixio
A1 Meijide Rodríguez, Adrián
K1 3311.02 Ingeniería de Control
K1 3305.22 Metrología de la Edificación
K1 3305.34 Topografía de la Edificación
AB Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.
PB Remote Sensing
SN 20724292
YR 2021
FD 2021-05-28
LK http://hdl.handle.net/11093/2518
UL http://hdl.handle.net/11093/2518
LA eng
NO Remote Sensing, 13(11): 2135 (2021)
NO Xunta de Galicia | Ref. ED431C 2016-038
DS Investigo
RD 09-dic-2023