Deep Learning-Based Automated Forest Health Diagnosis From Aerial Images
Deep Learning-Based Automated Forest Health Diagnosis From Aerial Images
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DOI：
10.1109/access.2020.3012417
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发表时间：
2020-07
期刊：
影响因子：
3.9
通讯作者：
Chia-Yen Chiang;Chloe Barnes;P. Angelov;Richard Jiang
中科院分区：
文献类型：
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作者：
Chia-Yen Chiang;Chloe Barnes;P. Angelov;Richard Jiang
全球气候变化对我们的环境产生了巨大影响。此前的研究表明，全球气候变化引发的病虫害可能导致大量树木死亡，不可避免地成为森林火灾的一个因素。森林火灾的一个重要预兆是森林的状况。基于航空图像的森林分析可以及早发现死树和活树。在本文中，我们应用了一种合成方法来扩大图像数据集，并提出了一种新的框架，使用重新训练的 Mask RCNN（基于掩模区域的卷积神经网络）方法和迁移学习方案，从航空图像中自动检测死树。我们将我们的框架应用于航空图像数据集，并比较八个微调模型。这些模型中最好的模型的平均精度得分 (mAP) 达到 54%。在自动检测之后，我们能够自动生成和计算死树掩模的数量，以标记图像中的死树，作为森林健康的指标，可以与环境变化的因果分析和森林火灾的预测可能性联系起来。
Global climate change has had a drastic impact on our environment. Previous study showed that pest disaster occured from global climate change may cause a tremendous number of trees died and they inevitably became a factor of forest fire. An important portent of the forest fire is the condition of forests. Aerial image-based forest analysis can give an early detection of dead trees and living trees. In this paper, we applied a synthetic method to enlarge imagery dataset and present a new framework for automated dead tree detection from aerial images using a re-trained Mask RCNN (Mask Region-based Convolutional Neural Network) approach, with a transfer learning scheme. We apply our framework to our aerial imagery datasets,and compare eight fine-tuned models. The mean average precision score (mAP) for the best of these models reaches 54%. Following the automated detection, we are able to automatically produce and calculate number of dead tree masks to label the dead trees in an image, as an indicator of forest health that could be linked to the causal analysis of environmental changes and the predictive likelihood of forest fire.