Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network

Volume: 32, Issue: 5, Pages: 960 - 979
Published: Feb 15, 2018
Abstract
Point cloud classification plays a critical role in many applications of airborne light detection and ranging (LiDAR) data. In this paper, we present a deep feature-based method for accurately classifying multiple ground objects from airborne LiDAR point clouds. With several selected attributes of LiDAR point clouds, our method first creates a group of multi-scale contextual images for each point in the data using interpolation. Taking the...
Paper Details
Title
Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network
Published Date
Feb 15, 2018
Volume
32
Issue
5
Pages
960 - 979
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