Cross-site learning in deep learning RGB tree crown detection
Cross-site learning in deep learning RGB tree crown detection
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DOI：
10.1016/j.ecoinf.2020.101061
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发表时间：
2020-03-01
影响因子：
5.1
通讯作者：
White, Ethan P.
中科院分区：
文献类型：
--
作者：
Weinstein, Ben G.;Marconi, Sergio;White, Ethan P.
树冠探测是林业和生态系统生态学遥感研究的一项基础性工作。虽然已经提出了许多单独的树木分割算法，但这些算法的开发和测试通常是针对特定地点的，很少有方法同时针对多个森林类型的数据进行评估。这使得很难确定所提出的方法的泛化，并限制了在大尺度上的树检测。利用来自国家生态观测站网络的数据，我们扩展了最近开发的深度学习方法，包括来自一系列森林类型的数据，以确定来自一个森林的信息是否可以用于其他森林的树木检测，并探索建立通用树木检测算法的潜力。我们发现深度学习方法可以很好地用于跨越森林条件的上层树木检测。在开阔栎树林中表现最好，在高山林中最差。当模型适合于一种森林类型并用于预测另一种森林类型时，性能通常会下降，当森林结构更相似时，性能会更好。然而，当模型在来自其他站点的数据上进行预训练，然后使用来自评估站点的相对少量的手工标记数据进行微调时，它们的表现与本地站点模型相似。最重要的是，适合所有站点数据的模型的表现与针对每个本地站点训练的单个模型一样好，甚至更好。
Tree crown detection is a fundamental task in remote sensing for forestry and ecosystem ecology. While many individual tree segmentation algorithms have been proposed, the development and testing of these algorithms is typically site specific, with few methods evaluated against data from multiple forest types simultaneously. This makes it difficult to determine the generalization of proposed approaches, and limits tree detection at broad scales. Using data from the National Ecological Observatory Network, we extend a recently developed deep learning approach to include data from a range of forest types to determine whether information from one forest can be used for tree detection in other forests, and explore the potential for building a universal tree detection algorithm. We find that the deep learning approach works well for overstory tree detection across forest conditions. Performance was best in open oak woodlands and worst in alpine forests. When models were fit to one forest type and used to predict another, performance generally decreased, with better performance when forests were more similar in structure. However, when models were pretrained on data from other sites and then finetuned using a relatively small amount of hand-labeled data from the evaluation site, they performed similarly to local site models. Most importantly, a model fit to data from all sites performed as well or better than individual models trained for each local site.