用激光点云生成伪标签，让模型自动分割树冠
Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels
- 用激光扫描数据生成伪标签，再通过SAM2增强
- 在无人工标注下实现优于现有通用模型的分割效果
- 适合城市绿化和森林监测等遥感应用
精准识别单个树冠对城市树木清查和森林健康监测至关重要。然而，由于纹理相似和部分树冠重叠，从航空影像中自动分离树冠仍具挑战。本文提出一种方法，利用机载激光扫描（ALS）数据生成伪标签，训练深度学习模型对RGB和多光谱图像进行树冠分割与分离。研究发现，通过零样本实例分割模型Segment Anything Model 2（SAM 2）可有效增强ALS生成的伪标签。该方法无需任何人工标注即可获得针对光学影像的领域特定训练数据，所构建的分割模型在相同任务上表现超越所有已发布的通用模型。
原文摘要 · Abstract (English)
Mapping individual tree crowns is essential for tasks such as maintaining urban tree inventories and monitoring forest health, which help us understand and care for our environment. However, automatically separating the crowns from each other in aerial imagery is challenging due to factors such as the texture and partial tree crown overlaps. In this study, we present a method to train deep learning models that segment and separate individual trees from RGB and multispectral images, using pseudo-labels derived from aerial laser scanning (ALS) data. Our study shows that the ALS-derived pseudo-labels can be enhanced using a zero-shot instance segmentation model, Segment Anything Model 2 (SAM 2). Our method offers a way to obtain domain-specific training annotations for optical image-based models without any manual annotation cost, leading to segmentation models which outperform any available models which have been targeted for general domain deployment on the same task.
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