论文标题
植被映射通过无人机可见图像和机器学习
Vegetation Mapping by UAV Visible Imagery and Machine Learning
论文作者
论文摘要
带有大量杂草的糖叶的实验田已用于测试无人机可见图像的植被鉴定。专家掩盖和色相过滤的图片已用于训练几种机器学习算法，以开发出高分辨率以识别和映射物种的半自动方法。结果表明，5M高度允许获得超过90％的识别效率的地图。这种方法可以很容易地集成以呈现VRHA，以及获得详细植被地图的工具。
An experimental field cropped with sugar-beet with a wide spreading of weeds has been used to test vegetation identification from drone visible imagery. Expert masked and hue-filtered pictures have been used to train several Machine Learning algorithms to develop a semi-automatic methodology for identification and mapping species at high resolution. Results show that 5m altitude allows for obtaining maps with an identification efficiency of more than 90%. Such a method can be easily integrated to present VRHA, as much as tools to obtain detailed maps of vegetation.
