INESC Coimbra, Department of Electrical and Computer Engineering, Polo 2, 3030 - 290 Coimbra, Portugal; University of Coimbra, Department of Mathematics, Apartado 3008 EC Santa Cruz, 3001 - 501 Coimbra, Portugal.
INESC Coimbra, Department of Electrical and Computer Engineering, Polo 2, 3030 - 290 Coimbra, Portugal.
Mar Pollut Bull. 2022 Mar;176:113431. doi: 10.1016/j.marpolbul.2022.113431. Epub 2022 Feb 12.
The use of Unmanned Aerial Systems (UAS, aka drones) has shown to be feasible to perform marine litter surveys. We operationally tested the use of multispectral images (5 bands) to classify litter type and material on a beach-dune system. For litter categorization by their multispectral characteristics, the Spectral Angle Mapping (SAM) technique was adopted. The SAM-based categorization of litter agreed with the visual classification, thus multispectral images can be used to fasten and/or making more robust the manual RGB image screening. Fully automated detection returned an F-score of 0.64, and a reasonable categorization of litter. Overall, the image-based litter density maps were in line with the manual detection. Assessments were promising given the complexity of the study area, where different dunes plants and partially-buried items challenged the UAS-based litter detection. The method can be easily implemented for both floating and beached litter, to advance litter survey in the environment.
无人航空系统（UAS，又名无人机）已被证明可用于进行海洋垃圾调查。我们在海滩沙丘系统上进行了多光谱图像（5 波段）分类垃圾类型和物质的操作测试。为了根据多光谱特征对垃圾进行分类，采用了光谱角制图（SAM）技术。基于 SAM 的垃圾分类与目视分类一致，因此多光谱图像可用于加快和/或使手动 RGB 图像筛选更稳健。全自动检测的 F 分数为 0.64，并且对垃圾进行了合理的分类。总体而言，基于图像的垃圾密度图与手动检测相符。鉴于研究区域的复杂性，评估结果很有希望，其中不同的沙丘植物和部分掩埋的物品对基于无人机的垃圾检测提出了挑战。该方法可轻松用于漂浮和搁浅的垃圾，以推进环境中的垃圾调查。
