Next Article in Journal
Associations between Benthic Cover and Habitat Complexity Metrics Obtained from 3D Reconstruction of Coral Reefs at Different Resolutions
Next Article in Special Issue
Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features
Previous Article in Journal
Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information
Previous Article in Special Issue
Annular Neighboring Points Distribution Analysis: A Novel PLS Stem Point Cloud Preprocessing Algorithm for DBH Estimation
Open AccessArticle

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

by Bingxiao Wu 1,2, Guang Zheng 1,2,* and Yang Chen 3
1
International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
2
Jiangsu Provincial Key Laboratory of Geographic Information Science, Nanjing 210023, China
3
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1010; https://doi.org/10.3390/rs12061010
Received: 19 January 2020 / Revised: 13 March 2020 / Accepted: 19 March 2020 / Published: 21 March 2020
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing)
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 this study, we developed a convolution neural network (CNN)-based model to separate foliage and woody components (FWCNN) by combing the geometrical and laser return intensity (LRI) information of local point sets in TLS datasets. Meanwhile, we corrected the LRI information and proposed a contribution score evaluation method to objectively determine hyper-parameters (learning rate, batch size, and validation split rate) in the FWCNN model. Our results show that: (1) Correcting the LRI information could improve the overall classification accuracy (OA) of foliage and woody points in tested broadleaf (from 95.05% to 96.20%) and coniferous (from 93.46% to 94.98%) TLS datasets (Kappa ≥ 0.86). (2) Optimizing hyper-parameters was essential to enhance the running efficiency of the FWCNN model, and the determined hyper-parameter set was suitable to classify all tested TLS data. (3) The FWCNN model has great potential to classify TLS data in mixed forests with OA > 84.26% (Kappa ≥ 0.67). This work provides a foundation for retrieving the structural features of woody materials within the forest canopy. View Full-Text
Keywords: point cloud classification; deep learning model; Terrestrial Laser Scanner (TLS); convolution neural network (CNN); hyper-parameter optimization point cloud classification; deep learning model; Terrestrial Laser Scanner (TLS); convolution neural network (CNN); hyper-parameter optimization
Show Figures

Graphical abstract

MDPI and ACS Style

Wu, B.; Zheng, G.; Chen, Y. An Improved Convolution Neural Network-Based Model for Classifying Foliage and Woody Components from Terrestrial Laser Scanning Data. Remote Sens. 2020, 12, 1010.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop