Next Article in Journal
Reconstruction of the Surface Inshore Labrador Current from SWOT Sea Surface Height Measurements
Next Article in Special Issue
Tree Height Estimation of Forest Plantation in Mountainous Terrain from Bare-Earth Points Using a DoG-Coupled Radial Basis Function Neural Network
Previous Article in Journal
Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery
Previous Article in Special Issue
Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data
Open AccessArticle

Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data

1
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
2
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02431 Masala, Finland
3
Centre of Excellence in Laser Scanning Research, Academy of Finland, 00531 Helsinki, Finland
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1263; https://doi.org/10.3390/rs11111263
Received: 21 April 2019 / Revised: 22 May 2019 / Accepted: 24 May 2019 / Published: 28 May 2019
(This article belongs to the Special Issue 3D Point Clouds in Forests)
Airborne lidar has been widely used for forest characterization to facilitate forest ecological and management studies. With the availability of increasingly higher point density, individual tree delineation (ITD) from airborne lidar point clouds has become a popular yet challenging topic, due to the complexity and diversity of forests. One important step of ITD is segmentation, for which various methodologies have been studied. Among them, a long proven image segmentation method, mean shift, has been applied directly onto 3D points, and has shown promising results. However, there are variations among those who implemented the algorithm in terms of the kernel shape, adaptiveness and weighting. This paper provides a detailed assessment of the mean shift algorithm for the segmentation of airborne lidar data, and the effect of crown top detection upon the validation of segmentation results. The results from three different datasets revealed that a crown-shaped kernel consistently generates better results (up to 7 percent) than other variants, whereas weighting and adaptiveness do not warrant improvements. View Full-Text
Keywords: individual tree detection; 3D clustering; airborne laser scanning; point cloud individual tree detection; 3D clustering; airborne laser scanning; point cloud
Show Figures

Graphical abstract

MDPI and ACS Style

Xiao, W.; Zaforemska, A.; Smigaj, M.; Wang, Y.; Gaulton, R. Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data. Remote Sens. 2019, 11, 1263. https://doi.org/10.3390/rs11111263

AMA Style

Xiao W, Zaforemska A, Smigaj M, Wang Y, Gaulton R. Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data. Remote Sensing. 2019; 11(11):1263. https://doi.org/10.3390/rs11111263

Chicago/Turabian Style

Xiao, Wen; Zaforemska, Aleksandra; Smigaj, Magdalena; Wang, Yunsheng; Gaulton, Rachel. 2019. "Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data" Remote Sens. 11, no. 11: 1263. https://doi.org/10.3390/rs11111263

Find Other Styles
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
Search more from Scilit
 
Search
Back to TopTop