Bark beetle outbreaks are a significant cause of loss of vegetation cover, for which accurate monitoring of forest areas is required to detect and control bark beetle outbreaks as early as possible. A tool that processes aerial imagery from an unmanned aerial vehicle to automatically detect levels of damage caused by bark beetle outbreaks is proposed and evaluated. The true-color RGB flight imagery is combined into orthomosaics, enhanced, and then analyzed in reference to manually annotated samples to identify thresholding rules for training classifiers based on a cellular automaton that assigns to each nonbackground pixel in the image a class label corresponding to the estimated stage of infestation at the location—healthy (green), early-stage (yellow), late-stage (red), and dead (leafless). Also samples corresponding to the ground (nontrees) are annotated and processed. The resulting classifications are on average over 89% accurate over five flights and often near flawless; the view from above does not fully substitute a ground-based assessment for intermediate stages of the infestation.

中文翻译：

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通过阈值自动机阈值检测无人机图像中的树皮甲虫侵扰

树皮甲虫暴发是植被丧失的重要原因，为此需要对森林面积进行准确监测，以尽早发现和控制树皮甲虫的暴发。提出并评估了一种处理来自无人机的航空影像以自动检测由树皮甲虫暴发引起的破坏程度的工具。真彩色RGB飞行图像被组合成正交图像，进行了增强，然后参考手动注释的样本进行分析，以识别基于分类器的阈值规则，用于训练分类器，该分类器基于细胞自动机，该自动机为图像中的每个非背景像素分配了对应于图像的类别标签。该位置的估计侵染阶段-健康（绿色），早期（黄色），晚期（红色）和死亡（无叶）。还注释和处理了对应于地面（非树）的样本。在五次飞行中得出的分类平均准确率超过89％，并且通常接近无瑕。上面的观点并不能完全代替地面评估来进行侵扰的中间阶段。