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Individual tree detection from airborne laser scanning (ALS) point clouds is the basis for forestry inventory and further applications. In the past decade, many methods have been developed to localize tree instances in ALS point clouds. These methods rely on empirical rules and field measurements that may change from plot to plot. Besides, most existing methods cannot consider multiple clues (e.g., shape priors and neighboring trees) under the same framework, which makes them not flexible and extensible. In this letter, we devise a new point-based and model-driven framework named “crown guess and selection”. This framework first generates crown candidates automatically, and then the qualities of candidates and their neighboring information are both considered. Finally, expected crowns are selected from candidates simultaneously. The proposed framework is tested and evaluated in a benchmark dataset. We also compare the new framework with several existing methods, and it turns out that the proposed framework outperforms others in terms of model flexibility and detection accuracy.

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.

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. [ABSTRACT FROM AUTHOR]