De Kock Meyer E, Pohůnek Václav, Hejcmanová Pavla
Czech University of Life Sciences Prague, Faculty of Tropical AgriSciences, Kamýcká 129, Praha-Suchdol, 165 00, Czech Republic; University of Pretoria, Faculty of Veterinary Science, Onderstepoort, Pretoria, 0110, South Africa.
University of Chemistry and Technology, Department of Food Preservation, Technická 3, Praha 6, 160 00, Czech Republic.
J Environ Manage. 2022 Oct 15;320:115807. doi: 10.1016/j.jenvman.2022.115807. Epub 2022 Aug 6.
In the field of species conservation, the use of unmanned aerial vehicles (UAV) is increasing in popularity as wildlife observation and monitoring tools. With large datasets created by UAV-based species surveying, the need arose to automate the detection process of the species. Although the use of computer learning algorithms for wildlife detection from UAV-derived imagery is an increasing trend, it depends on a large amount of imagery of the species to train the object detector effectively. However, there are alternatives like object-based image analysis (OBIA) software available if a large amount of imagery of the species is not available to develop a computer-learned object detector. The study tested the semi-automated detection of reintroduced Arabian Oryx (O. leucoryx), using the specie's coat sRGB-colour profiles as input for OBIA to identify adult O. leucoryx, applied to UAV acquired imagery. Our method uses lab-measured spectral reflection of hair sample values, collected from captive O. leucoryx as an input for OBIA ruleset to identify adult O. leucoryx from UAV survey imagery using semi-automated supervised classification. The converted mean CIE Lab reflective spectrometry colour values of n = 50 hair samples of adult O. leucoryx to 8-bit sRGB-colour profiles of the species resulted in the red-band value of 157.450, the green-band value of 151.390 and blue-band value of 140.832. The sRGB values and a minimum size permitter were added as the input of the OBIA ruleset identified adult O. leucoryx with a high degree of efficiency when applied to three UAV census datasets. Using species sRGB-colour profiles to identify re-introduced O. leucoryx and extract location data using a non-invasive UAV-based tool is a novel method with enormous application possibilities. Coat refection sRGB-colour profiles can be developed for a range of species and customised to autodetect and classify the species from remote sensing data.
在物种保护领域，无人驾驶飞行器（UAV）作为野生动物观测和监测工具越来越受欢迎。随着基于无人机的物种调查产生了大量数据集，人们开始需要自动化物种检测过程。尽管使用计算机学习算法从无人机获取的图像中检测野生动物的趋势日益增加，但这依赖于大量该物种的图像来有效地训练目标检测器。然而，如果没有大量该物种的图像来开发计算机学习的目标检测器，也有基于对象的图像分析（OBIA）软件等替代方法。该研究测试了对重新引入的阿拉伯羚羊（O. leucoryx）的半自动检测，使用该物种皮毛的sRGB颜色配置文件作为OBIA的输入，以识别成年阿拉伯羚羊，并将其应用于无人机获取的图像。我们的方法使用从圈养的阿拉伯羚羊身上采集的毛发样本的实验室测量光谱反射值，作为OBIA规则集的输入，通过半自动监督分类从无人机调查图像中识别成年阿拉伯羚羊。将n = 50个成年阿拉伯羚羊毛发样本的转换后的平均CIE Lab反射光谱颜色值转换为该物种的8位sRGB颜色配置文件，得到红色波段值为157.450，绿色波段值为151.390，蓝色波段值为140.832。当将sRGB值和最小尺寸允许值作为OBIA规则集的输入应用于三个无人机普查数据集时，能够高效地识别成年阿拉伯羚羊。使用物种的sRGB颜色配置文件来识别重新引入的阿拉伯羚羊，并使用基于无人机的非侵入性工具提取位置数据，是一种具有巨大应用潜力的新方法。可以为一系列物种开发皮毛反射sRGB颜色配置文件，并进行定制，以从遥感数据中自动检测和分类物种。
