中文标题#
基于机载激光雷达点云的树实例分割的弱监督学习
英文标题#
Weakly-Supervised Learning for Tree Instances Segmentation in Airborne Lidar Point Clouds
中文摘要#
机载激光扫描（ALS）数据的树木实例分割对于森林监测至关重要，但由于传感器分辨率、采集时植被状态、地形特征等因素导致的数据变化，仍然具有挑战性。 此外，获取足够精确标记的数据来训练完全监督的实例分割方法成本很高。 为了解决这些挑战，我们提出了一种弱监督方法，其中通过人工操作员提供的初始分割结果的标签作为质量评分，该初始分割结果可以通过非微调模型或闭式算法获得。 在质量评估过程中生成的标签随后用于训练一个评分模型，其任务是将分割输出分类为与人工操作员指定的相同类别。 最后，使用评分模型的反馈对分割模型进行微调。 这反过来使原始分割模型在正确识别的树木实例方面提高了 34%，同时显著减少了预测的非树木实例数量。 在以小树（高度小于两米）或包含灌木、岩石等复杂环境为特征的稀疏森林区域，仍然存在挑战，这些区域可能会被误认为树木，导致所提出方法的性能下降。
英文摘要#
Tree instance segmentation of airborne laser scanning (ALS) data is of utmost importance for forest monitoring, but remains challenging due to variations in the data caused by factors such as sensor resolution, vegetation state at acquisition time, terrain characteristics, etc. Moreover, obtaining a sufficient amount of precisely labeled data to train fully supervised instance segmentation methods is expensive. To address these challenges, we propose a weakly supervised approach where labels of an initial segmentation result obtained either by a non-finetuned model or a closed form algorithm are provided as a quality rating by a human operator. The labels produced during the quality assessment are then used to train a rating model, whose task is to classify a segmentation output into the same classes as specified by the human operator. Finally, the segmentation model is finetuned using feedback from the rating model. This in turn improves the original segmentation model by 34% in terms of correctly identified tree instances while considerably reducing the number of non-tree instances predicted. Challenges still remain in data over sparsely forested regions characterized by small trees (less than two meters in height) or within complex surroundings containing shrubs, boulders, etc. which can be confused as trees where the performance of the proposed method is reduced.
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