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首页> 外文期刊>Estuarine Coastal and Shelf Science >A novel index to detect green-tide using UAV-based RGB imagery
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A novel index to detect green-tide using UAV-based RGB imagery

机译：使用基于UV的RGB图像来检测绿色潮汐的新索引

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摘要

Unmanned aerial vehicles (UAV) equipped with high-resolution camera have been increasingly applied in environment monitoring as an important complement to traditional satellite remote sensing. An accurate extraction of marine green-tide regions still faces many technological challenges, such as the absence of centimeter-level orthophoto maps and a dedicated green-tide index based on red-green-blue (RGB) bands. In this study, a new green-tide index, namely, the red-green-blue floating algae index (RGB-FAI) using RGB images captured by ship-borne UAV, is developed for green-tide detection in the Yellow Sea, China. Specifically, RGB-FAI is defined to measure the green-reflectance height by using the red and blue bands as the baselines. Our results show that the RGB-FAI performs well in the detection of green-tide and the accuracy is satisfactory (kappa = 0.95). It is worthy to note that RGB-FAI has the highest extraction accuracy among these competing indices for green-tide in the declining phase under a hazy atmospheric condition. In addition, by combining the bi-temporal UAV images with RGB-FAI, the drift velocity of green-tide has also been estimated as 0.26 m/s in a 17.1 degrees east by north during aerial photography. In conclusion, the proposed RGB-FAI is effective for green-tide detection and has more potential usage in marine environment monitoring.
机译：装备高分辨率摄像头的无人机（UAV）越来越多地应用于环境监测，作为传统卫星遥感的重要补充。准确提取海洋绿色潮汐地区仍然面临许多技术挑战，例如缺乏厘米级正射周地图和基于红绿蓝（RGB）频段的专用绿色潮汐指数。在这项研究中，新的绿色潮汐指数，即使用由舰载UAV捕获的RGB图像的红绿浮藻指数（RGB-FAI）是为中国黄海的绿潮检测开发的。具体而言，RGB-FAI被定义为通过使用红色和蓝色带作为基线来测量绿色反射高度。我们的研究结果表明，RGB-FAI在潮汐检测中表现良好，精度令人满意（Kappa = 0.95）。值得注意的是，RGB-FAI在朦胧大气条件下的下降阶段的竞争指数中具有最高的提取准确性。另外，通过将双颞UAV图像与RGB-FAI组合，在航空摄影期间北北北北面的绿色潮汐漂移速度也估计为0.26米/秒。总之，拟议的RGB-FAI对绿色潮汐检测有效，在海洋环境监测中具有更多潜在的使用情况。

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  • 来源
    《Estuarine Coastal and Shelf Science》 |2020年第30期|106943.1-106943.8|共8页
  • 作者

    Jiang Xiaopeng; Gao Meng; Gao Zhiqiang;

  • 作者单位

    Chinese Acad Sci Yantai Inst Coastal Zone Res CAS Key Lab Coastal Environm Proc & Ecol Remediat Yantai 264003 Shandong Peoples R China|Chinese Acad Sci Yantai Inst Coastal Zone Res Shandong Key Lab Coastal Environm Proc Yantai 264003 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China|Chinese Acad Sci Ctr Ocean Megasci Qingdao 266400 Shandong Peoples R China;

    Chinese Acad Sci Yantai Inst Coastal Zone Res CAS Key Lab Coastal Environm Proc & Ecol Remediat Yantai 264003 Shandong Peoples R China|Chinese Acad Sci Yantai Inst Coastal Zone Res Shandong Key Lab Coastal Environm Proc Yantai 264003 Peoples R China|Chinese Acad Sci Ctr Ocean Megasci Qingdao 266400 Shandong Peoples R China;

    Chinese Acad Sci Yantai Inst Coastal Zone Res CAS Key Lab Coastal Environm Proc & Ecol Remediat Yantai 264003 Shandong Peoples R China|Chinese Acad Sci Yantai Inst Coastal Zone Res Shandong Key Lab Coastal Environm Proc Yantai 264003 Peoples R China|Chinese Acad Sci Ctr Ocean Megasci Qingdao 266400 Shandong Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Unmanned aerial vehicle (UAV); RGB-FAI; Remote sensing; Green tide; Drift velocity estimation;

    机译：无人驾驶飞行器（UAV）;RGB-FAI;遥感;绿色潮;漂移速度估计;

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