Vision based crown loss estimation for individual trees with remote aerial robots
Vision based crown loss estimation for individual trees with remote aerial robots
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DOI：
10.1016/j.isprsjprs.2022.04.002
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发表时间：
2022-06
影响因子：
12.7
通讯作者：
B. Ho;Basaran Bahadir Kocer;M. Kovač
中科院分区：
文献类型：
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作者：
B. Ho;Basaran Bahadir Kocer;M. Kovač
由于能够捕获高分辨率图像数据和易于进入偏远地区，空中机器人在森林健康监测应用中越来越受欢迎。例如，林业任务，如实地调查和叶面取样，通常是手工和劳动密集型的，可以用遥控空中机器人实现自动化。在这项研究中，我们提出了两个新的在线框架来量化和排名单个树冠损失的严重程度。实时树冠损失估计（RTCLE）模型将单个树木定位并分类到各自的树冠损失百分比箱中。由于具有不同视点的真实图像通常收集成本很高，因此进行了实验来研究合成生成的树状图像是否可以用于训练RTCLE模型。结果表明，合成数据训练有助于获得令人满意的基线平均精度（mAP），并且可以通过一些额外的真实图像数据进一步提高。我们发现，通过将真实数据集与生成的合成数据混合，mAP可以从60%增加到78%。对于单株树冠损失排序，提出了一种两步树冠损失排序框架来处理标记不一致的树冠损失数据。TSCLR框架检测单个树木，然后根据一些相对树冠损失严重程度指标对它们进行排名。树检测模型是用RTCLE模型训练中使用的组合数据集进行训练的，我们在RTCLE模型训练中获得了大约95%的mAP，这表明该模型可以很好地推广到未见过的数据集。每棵树的相对树冠损失严重程度是通过深度表示学习，通过一个完全训练的变分自编码器（VAE）模型的概率编码器来估计的。对VAE进行端到端训练，以不受背景影响的方式重建树状图像。基于保守评估，概率编码器估计的树冠损失严重程度总体上与数据集中存在的所有树种的专家估计一致。所有的软件管道、数据集和合成数据集生成都可以在GitHub链接中找到。
With the capability of capturing high-resolution imagery data and the ease of accessing remote areas, aerial robots are becoming increasingly popular for forest health monitoring applications. For example, forestry tasks such as field surveys and foliar sampling which are generally manual and labour intensive can be automated with remotely controlled aerial robots. In this study, we propose two new online frameworks to quantify and rank the severity of individual tree crown loss. The real-time crown loss estimation (RTCLE) model localises and classifies individual trees into their respective crown loss percentage bins. Experiments are conducted to investigate if synthetically generated tree images can be used to train the RTCLE model as real images with diverse viewpoints are generally expensive to collect. Results have shown that synthetic data training helps to achieve a satisfactory baseline mean average precision (mAP) which can be further improved with just some additional real imagery data. We showed that the mAP can be increased approximately from 60% to 78% by mixing the real dataset with the generated synthetic data. For individual tree crown loss ranking, a two-step crown loss ranking (TSCLR) framework is developed to handle the inconsistently labelled crown loss data. The TSCLR framework detects individual trees before ranking them based on some relative crown loss severity measures. The tree detection model is trained with the combined dataset used in the RTCLE model training where we achieved an mAP of approximately 95% suggesting that the model generalises well to unseen datasets. The relative crown loss severity of each tree is estimated, with deep representation learning, by a probabilistic encoder from a fully trained variational autoencoder (VAE) model. The VAE is trained end-to-end to reconstruct tree images in a background agnostic way. Based on a conservative evaluation, the estimated crown loss severity from the probabilistic encoder generally showed moderate agreement with the expert’s estimation across all species of trees present in the dataset. All the software pipelines, the dataset, and the synthetic dataset generation can be found in the GitHub link.