无需标注，用生物图像流场方法实现林木树冠精准分割。
FG-TreeSeg: Flow-Guided Tree Crown Segmentation without Instance Annotations
- 利用生物图像中的流场机制，将树冠视为星凸形对象进行分离。
- 在NEON和BAMFOREST数据集上实现跨传感器、高密度树冠的稳定分割。
- 训练免调，适合缺乏标注数据的森林监测场景。
个体树冠分割在遥感中对森林生物量估算和生态监测至关重要。然而，密集重叠树冠的精确分割仍是瓶颈。尽管监督深度学习方法存在标注成本高、泛化能力差的问题，新兴基础模型（如Segment Anything Model）常因缺乏领域知识导致密集区域分割不足。为此，我们提出FG-TreeSeg，一种无需训练的树冠实例分割框架，将生物医学图像中的流场分割思想迁移至遥感。通过在拓扑流场中建模树冠为星凸形对象，并利用Cellpose-SAM实现向量收敛引导分离，有效区分重叠树冠。在NEON与BAMFOREST数据集上的实验及视觉检验表明，该框架在不同传感器类型和树冠密度下均具备鲁棒泛化能力，可提供无需训练的树冠实例分割与标签生成方案。
原文摘要 · Abstract (English)
Individual tree crown segmentation is an important task in remote sensing for forest biomass estimation and ecological monitoring. However, accurate delineation in dense, overlapping canopies remains a bottleneck. While supervised deep learning methods suffer from high annotation costs and limited generalization, emerging foundation models (e.g., Segment Anything Model) often lack domain knowledge, leading to under-segmentation in dense clusters. To bridge this gap, we propose FG-TreeSeg, a training-free framework for tree crown instance segmentation that transfers flow-based delineation from biomedical imaging to remote sensing. By modeling tree crowns as star-convex objects within a topological flow field using Cellpose-SAM, the FG-TreeSeg framework forces the separation of touching tree crown instances based on vector convergence. Experiments on the NEON and BAMFOREST datasets and visual inspection demonstrate that our framework generalizes robustly across diverse sensor types and canopy densities, which can offer a training-free solution for tree crown instance segmentation and labels generation.
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