基于增强型激光雷达伪标签学习图像驱动的树冠分割

摘要

单株树冠测绘对于维护城市树木清单和监测森林健康等工作至关重要，这些工作有助于我们理解和保护环境。然而，由于树木纹理特征和树冠部分重叠等因素，在航拍图像中自动分离单株树冠具有挑战性。本研究提出一种基于航空激光扫描（ALS）数据生成的伪标签，训练深度学习模型从RGB和多光谱图像中分割单株树木的方法。研究表明，利用零样本实例分割模型SAM 2（Segment Anything Model 2）能够增强ALS衍生的伪标签质量。该方法无需人工标注成本即可为光学图像模型获取特定领域的训练标注，最终训练出的分割模型在相同任务上超越了所有针对通用领域部署的现有模型。

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.

基于增强型激光雷达伪标签学习图像驱动的树冠分割

Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels

摘要

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