用无人机影像自动分割树冠，探索SAM模型的适用性与改进空间。
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
- 基于SAM构建树冠实例分割方法，结合精心设计的提示词
- 原生SAM表现不如定制的Mask R-CNN，但微调后有潜力提升
- 融合数字地表模型（DSM）信息可显著改善分割效果
植树作为自然气候解决方案的潜力常因项目监测不足而受限。当前监测依赖人工逐株测量，耗时耗力。无人机遥感与计算机视觉的发展为从航拍影像中自动测绘和识别树木提供了可能。大型预训练视觉模型如分割任意模型（SAM）在标注数据有限的情况下尤为吸引人。本文对比了多种基于SAM的方法，在高分辨率无人机影像中对幼龄林分树冠进行实例分割。结果表明，直接使用SAM性能仍不及定制的Mask R-CNN，即使采用优化提示也未超越；但进一步微调SAM具备提升潜力。同时，将数字地表模型（DSM）作为输入能有效改善预测精度。
原文摘要 · Abstract (English)
The potential of tree planting as a natural climate solution is often undermined by inadequate monitoring of tree planting projects. Current monitoring methods involve measuring trees by hand for each species, requiring extensive cost, time, and labour. Advances in drone remote sensing and computer vision offer great potential for mapping and characterizing trees from aerial imagery, and large pre-trained vision models, such as the Segment Anything Model (SAM), may be a particularly compelling choice given limited labeled data. In this work, we compare SAM methods for the task of automatic tree crown instance segmentation in high resolution drone imagery of young tree plantations. We explore the potential of SAM for this task, and find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts, but that there is potential for methods which tune SAM further. We also show that predictions can be improved by adding Digital Surface Model (DSM) information as an input.
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