融合高程数据提升无人机影像树木冠层分割精度
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
- 用SAM结合数字表面模型（DSM）实现树冠实例分割
- 在寒带林地场景中，新模型性能优于传统方法
- 适合林业遥感与生态监测研究者参考
个体树木信息对森林生态系统监测与管理至关重要。传统地面测量耗时耗力，而无人机遥感与计算机视觉为大范围个体树木制图提供了新可能。本文比较了基于预训练视觉模型SAM在三种典型场景（寒带人工林、温带林、热带林）中自动分割树冠的能力，并探索将从彩色无人机影像直接获取的数字表面模型（DSM）信息融入模型的方法。提出BalSAM模型，结合SAM与DSM，在人工林场景中表现突出。结果表明，直接使用SAM并配合精心设计提示，仍不如定制化的Mask R-CNN；但端到端优化SAM并融合高程信息，是提升树冠分割效果的可行路径。
原文摘要 · Abstract (English)
Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring methods involve ground measurements, requiring extensive cost, time and labor. Advances in drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale. Large pre-trained vision models, such as the Segment Anything Model (SAM), represent a particularly compelling choice given limited labeled data. In this work, we compare methods leveraging SAM for the task of automatic tree crown instance segmentation in high resolution drone imagery in three use cases: 1) boreal plantations, 2) temperate forests and 3) tropical forests. We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery. We present BalSAM, a model leveraging SAM and DSM information, which shows potential over other methods, particularly in the context of plantations. We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts. However, efficiently tuning SAM end-to-end and integrating DSM information are both promising avenues for tree crown instance segmentation models.
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