SPA-Net: An Offset-Free Proposal Network for Individual Tree Segmentation from TLS Data
Individual tree segmentation (ITS) from terrestrial laser scanning (TLS) point clouds is foundational for deriving detailed forest structural parameters, crucial for precision forestry, biomass calculation, and carbon accounting. Conventional ITS algorithms often struggle in complex forest stands du...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/13/2292 |
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| Summary: | Individual tree segmentation (ITS) from terrestrial laser scanning (TLS) point clouds is foundational for deriving detailed forest structural parameters, crucial for precision forestry, biomass calculation, and carbon accounting. Conventional ITS algorithms often struggle in complex forest stands due to reliance on heuristic rules and manual feature engineering. Deep learning methodologies proffer more efficacious and automated solutions, but their segmentation accuracy is restricted by imprecise center offset predictions, particularly in intricate forest environments. To address this issue, we proposed a deep learning method, SPA-Net, for achieving tree instance segmentation of forest point clouds. Unlike methods heavily reliant on potentially error-prone global offset vector predictions, SPA-Net employs a novel sampling-shifting-grouping paradigm within its sparse geometric proposal (SGP) module to directly generate initial proposal candidates from raw point data, aiming to reduce dependence on the offset branch. Subsequently, an affinity aggregation (AA) module robustly refines these proposals by assessing inter-proposal relationships and merging fragmented segments, effectively mitigating oversegmentation of large or complex trees; integrating with SGP eliminates the postprocessing step of scoring/NMS. SPA-Net was rigorously validated on two different forest datasets. On both BaiMa and Hong-Tes Lake datasets, the approach demonstrated superior performance compared to several contemporary segmentation approaches evaluated under the same conditions. It achieved 95.8% precision, 96.3% recall, and 92.9% coverage on BaiMa dataset, and achieved 92.6% precision, 94.8% recall, and 88.8% coverage on the Hong-Tes Lake dataset. This study provides a robust tool for individual tree analysis, advancing the accuracy of individual tree segmentation in challenging forest environments. |
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| ISSN: | 2072-4292 |