利用分割阈值法对无人机图像进行自动解释，实现海滩垃圾监测。
Monitoring of beach litter by automatic interpretation of unmanned aerial vehicle images using the segmentation threshold method.
机构信息
State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou, China; School of Geographical Sciences, Fujian Normal University, Fuzhou, China; Investigation and Surveying Institute, Fuzhou, China.
State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou, China; School of Geographical Sciences, Fujian Normal University, Fuzhou, China; China-Europe Center for Environment and Landscape Management, Fuzhou, China.
出版信息
Mar Pollut Bull. 2018 Dec;137:388-398. doi: 10.1016/j.marpolbul.2018.08.009. Epub 2018 Oct 23.
This study was aimed at monitoring beach litter using an unmanned aerial vehicle (UAV) in the coastal city of Fuzhou, China. The data analysis shows that the optical images obtained by digital cameras on the UAV can help to identify and monitor beach litter using remote sensing and GIS technologies. The threshold method can effectively segment the UAV image in the beach area. It is useful for quickly monitoring the distribution of beach litter in the area of interest, and hence it can help to provide effective technical support for the investigation and assessment of coastal beach litter.
本研究旨在利用无人机（UAV）在中国沿海城市福州监测海滩垃圾。数据分析表明，UAV 上的数码相机获取的光学图像可借助遥感和 GIS 技术来识别和监测海滩垃圾。阈值方法可有效分割海滩区域的 UAV 图像，有助于快速监测感兴趣区域内海滩垃圾的分布情况，从而为调查和评估沿海海滩垃圾提供有效的技术支持。
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