Journal
REMOTE SENSING
Volume 13, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/rs13030338
Keywords
tree localization; tree instances; point cloud transformation; local maximums
Categories
Funding
- Natural Science Foundation of Jiangsu Province [BK20200784]
- National Natural Science Foundation of China [41971415]
- China Postdoctoral Science Foundation [2019M661852]
- Talent Startup Project of Zhejiang A & F University Scientific Research Development Foundation [2034020104]
Ask authors/readers for more resources
This study aims to make top-based methods applicable to TLS forest scenes by proposing a novel point cloud transformation. The method is tested on an international benchmark, demonstrating its necessity and effectiveness without requiring additional preprocessing steps. It has the potential to benefit other object localization tasks in different scenes based on detailed analysis and tests.
Tree localization in point clouds of forest scenes is critical in the forest inventory. Most of the existing methods proposed for TLS forest data are based on model fitting or point-wise features which are time-consuming, sensitive to data incompleteness and complex tree structures. Furthermore, these methods often require lots of preprocessing such as ground filtering and noise removal. The fast and easy-to-use top-based methods that are widely applied in processing ALS point clouds are not applicable in localizing trees in TLS point clouds due to the data incompleteness and complex canopy structures. The objective of this study is to make the top-based methods applicable to TLS forest point clouds. To this end, a novel point cloud transformation is presented, which enhances the visual salience of tree instances and makes the top-based methods adapting to TLS forest scenes. The input for the proposed method is the raw point clouds and no other pre-processing steps are needed. The new method is tested on an international benchmark and the experimental results demonstrate its necessity and effectiveness. Finally, the proposed method has the potential to benefit other object localization tasks in different scenes based on detailed analysis and tests.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available