Probabilistic Prior-Constrained Instance Reconstruction for Individual Tree Crown Segmentation in Minimally Annotated Forest Plots.
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| Title: | Probabilistic Prior-Constrained Instance Reconstruction for Individual Tree Crown Segmentation in Minimally Annotated Forest Plots. |
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| Authors: | Wang, Zhihao1 (AUTHOR), Zhou, Hang1 (AUTHOR), Zhu, Yunjie1 (AUTHOR), Yang, Suyu1 (AUTHOR), Hu, Chunhua1 (AUTHOR) huchunhua@njfu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p2054. 23p. |
| Subjects: | Crowns (Botany), Coniferous forests, Forest reserves |
| Abstract: | Highlights: What are the main findings? A probabilistic prior-constrained reconstruction framework treats semantic segmentation output as an interpretable canopy prior and organizes instance recovery through HCSM, support-domain-constrained recovery, and selective splitting. On a single 500 × 500 m mixed conifer–broadleaf natural secondary forest plot with 306 retained reference crowns, the high-Recall VORv1 branch increases Recall from 0.369 to 0.673 over the internal R2 baseline, while the balanced E2GROW configuration achieves the highest F1_proxy with fewer predicted objects. These values quantify within-plot diagnostic behavior for the current site; independent cross-site validation remains outside the present experimental scope. What is the implication of the main finding? Meaningful instance-level crown reconstruction can be achieved under severe annotation scarcity and heavy canopy adhesion by combining semantic priors, height information, and structured object-level reasoning. The framework is most applicable to annotation-scarce closed-canopy plots with a usable semantic canopy prior and height information; cross-site generalization requires multi-site validation. Individual tree crown (ITC) segmentation in structurally complex mixed forests remains challenging under limited annotation, uneven effective height-structure support, and severe inter-crown adhesion. Existing end-to-end instance segmentation methods often require substantial instance-level annotation, and their cross-domain transferability can degrade when applied to plots with different forest structures. This study proposes a probabilistic prior-constrained instance reconstruction framework that treats semantic segmentation output as an interpretable canopy prior and reconstructs object-level crowns through a structured post-processing pipeline. A height-aware canopy support mask (HCSM) converts the probability field into a credible operational domain through hysteresis thresholding, morphological reconstruction, and a height constraint. Constrained recovery within the support domain (E2GROW) repairs coverage deficiency through spatially bounded boundary adjustment with guard rails on area ratio and buffer distance. Selective splitting then addresses residual merge errors through branch-specific seed-guided partitioning, including an aggressive Voronoi reference branch and a more conservative LOCAL/marker-controlled watershed branch with explicit trigger and child-object filtering criteria. An instance-level evaluation loop based on Gate-3 Recall, a precision proxy, and threshold-crossing audits is used during module development as an iterative safeguard. On a single 500 × 500 m mixed conifer–broadleaf plot with 306 reference crowns retained for evaluation, the high-Recall VORv1 branch improves Recall from 0.369 to 0.673 over the internal R2 baseline produced by the semantic-prior-to-instance initialization procedure, whereas the balanced E2GROW configuration achieves the highest F1_proxy with fewer predicted objects; the overall gain originates from two distinct mechanisms: threshold-crossing boundary recovery for coverage-deficient crowns and local structural decomposition for merged crown groups. Sensitivity analysis indicates that the support-domain construction is stable across the explored parameter ranges, and that the two splitting branches realize a structural Recall–precision trade-off with no evidence of simple additive gains. The framework is modular and auditable, and its demonstrated applicability is strongest for annotation-scarce closed-canopy plots where a usable semantic canopy prior and height information are available. The reported evidence represents a single-site, within-plot methodological demonstration. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A probabilistic prior-constrained reconstruction framework treats semantic segmentation output as an interpretable canopy prior and organizes instance recovery through HCSM, support-domain-constrained recovery, and selective splitting. On a single 500 × 500 m mixed conifer–broadleaf natural secondary forest plot with 306 retained reference crowns, the high-Recall VORv1 branch increases Recall from 0.369 to 0.673 over the internal R2 baseline, while the balanced E2GROW configuration achieves the highest F1_proxy with fewer predicted objects. These values quantify within-plot diagnostic behavior for the current site; independent cross-site validation remains outside the present experimental scope. What is the implication of the main finding? Meaningful instance-level crown reconstruction can be achieved under severe annotation scarcity and heavy canopy adhesion by combining semantic priors, height information, and structured object-level reasoning. The framework is most applicable to annotation-scarce closed-canopy plots with a usable semantic canopy prior and height information; cross-site generalization requires multi-site validation. Individual tree crown (ITC) segmentation in structurally complex mixed forests remains challenging under limited annotation, uneven effective height-structure support, and severe inter-crown adhesion. Existing end-to-end instance segmentation methods often require substantial instance-level annotation, and their cross-domain transferability can degrade when applied to plots with different forest structures. This study proposes a probabilistic prior-constrained instance reconstruction framework that treats semantic segmentation output as an interpretable canopy prior and reconstructs object-level crowns through a structured post-processing pipeline. A height-aware canopy support mask (HCSM) converts the probability field into a credible operational domain through hysteresis thresholding, morphological reconstruction, and a height constraint. Constrained recovery within the support domain (E2GROW) repairs coverage deficiency through spatially bounded boundary adjustment with guard rails on area ratio and buffer distance. Selective splitting then addresses residual merge errors through branch-specific seed-guided partitioning, including an aggressive Voronoi reference branch and a more conservative LOCAL/marker-controlled watershed branch with explicit trigger and child-object filtering criteria. An instance-level evaluation loop based on Gate-3 Recall, a precision proxy, and threshold-crossing audits is used during module development as an iterative safeguard. On a single 500 × 500 m mixed conifer–broadleaf plot with 306 reference crowns retained for evaluation, the high-Recall VORv1 branch improves Recall from 0.369 to 0.673 over the internal R2 baseline produced by the semantic-prior-to-instance initialization procedure, whereas the balanced E2GROW configuration achieves the highest F1_proxy with fewer predicted objects; the overall gain originates from two distinct mechanisms: threshold-crossing boundary recovery for coverage-deficient crowns and local structural decomposition for merged crown groups. Sensitivity analysis indicates that the support-domain construction is stable across the explored parameter ranges, and that the two splitting branches realize a structural Recall–precision trade-off with no evidence of simple additive gains. The framework is modular and auditable, and its demonstrated applicability is strongest for annotation-scarce closed-canopy plots where a usable semantic canopy prior and height information are available. The reported evidence represents a single-site, within-plot methodological demonstration. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18122054 |
