将SAM提升至新高度：利用高程数据从无人机影像中进行树冠分割
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
AI总结：
本研究针对无人机影像树冠实例分割任务，对比不同SAM方法，提出整合DSM的BALSAM模型，发现SAM端到端调优与DSM整合具应用潜力。
摘要：
单株树木信息对森林生态系统监测和森林管理规划至关重要。当前监测方法涉及地面测量，需大量成本、时间和人力。无人机遥感与计算机视觉的进展为从航空影像进行大范围单株树木制图提供了巨大潜力。鉴于标注数据有限，大型预训练视觉模型如Segment Anything Model（SAM）是极具吸引力的选择。本研究在三个应用场景中比较了利用SAM进行高分辨率无人机影像自动树冠实例分割的方法：1）北方人工林、2）温带森林、3）热带森林。我们还研究了将高程数据以数字表面模型（DSM）信息的形式整合入模型的方式，该信息可从RGB无人机影像轻易获取且无需额外成本。我们提出了BALSAM模型，该模型利用SAM和DSM信息，相较于其他方法展现出潜力，尤其在人工林场景中表现突出。我们发现，开箱即用的SAM方法即便采用精心设计的提示，也未优于定制的Mask R-CNN；然而，对SAM进行高效端到端调优并整合DSM信息，均是树冠实例分割模型的有前景方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
