Classification of compressed full-waveform airborne lidar data

ABSTRACT

Airborne laser scanners produce 3D data that can be used for a range of applications, such as urban planning, facility monitoring, flood mapping, and forest management. Additional information on the surveyed area can be obtained from the backscattered waveforms recorded by modern light detection and ranging (lidar) sensors. However, the high-dimensional representation of full-waveform data has hindered progress in its use due to difficulties in processing and storage. This paper develops a quantized convolutional autoencoder network to compress lidar waveform data into a condensed feature representation, resulting in a compression rate of up to 20:1. This, together with height information, is fed into a U-net convolutional neural network that achieves an accuracy of 93.7% on six classes.

KEYWORDS:

• Lidar
• full-waveform
• data compression
• classification

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available at the link https://uniudamce-my.sharepoint.com/:f:/g/personal/compvis_uniud_it/EshMZziamr5MjSIcqxhiesUB3R0EnYDnQbFdp_ae-K6xvQ?e=oEnxzN.

Additional information

Funding

S.O.M. has been supported by the Italian “MISE PoC 2020” project for the exploitation of IPs, with reference to the EU Patent N. 3.794.376 “Apparatus and method for classification of backscattered full-waveform signals”.