An Improved Convolution Neural Network-Based Model for Classifying Foliage and Woody Components from Terrestrial Laser Scanning Data

An Improved Convolution Neural Network-Based Model for Classifying Foliage and Woody Components from Terrestrial Laser Scanning Data

Separating foliage and woody components can effectively improve the accuracy of simulating the forest eco-hydrological processes. It is still challenging to use deep learning models to classify canopy components from the point cloud data collected in forests by terrestrial laser scanning (TLS). In t...

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Bibliographic Details
Main Authors: Bingxiao Wu, Guang Zheng, Yang Chen
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Remote Sensing
Subjects:
point cloud classification
deep learning model
terrestrial laser scanner (tls)
convolution neural network (cnn)
hyper-parameter optimization
Online Access:https://www.mdpi.com/2072-4292/12/6/1010

Internet

https://www.mdpi.com/2072-4292/12/6/1010

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