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面向对象的无人机遥感影像岸线提取方法研究
麻德明1, 刘焱雄1, 金永德1, 程宝权2
1.自然资源部第一海洋研究所;2.青建集团股份公司
摘要:
海岸线作为重要的自然资源,在沿海经济发展中发挥着重要的作用,如何快速、准确地提取海岸线的位置及其动态变化信息,是自然资源主管部门和科研工作者一直关注的问题。本文选取青岛小岛湾海岸线为研究区,以无人机遥感影像为基础数据,提出一种面向对象的海岸线提取方法,结合现场实测验证,开展了人工海岸线和砂质海岸线识别的应用实验。结果表明:人工海岸线和砂质海岸线概率边缘指数PRI分别为0.97和0.88,边缘定位误差BDE分别为4.33和2.84,提取的人工海岸线和砂质海岸线与实测海岸线结果整体上匹配较好,仅在局部细微处存在微小差异。本文提出的方法可快速有效地获取海岸线信息,其精度能够满足海岸线动态变化监测的需求,可在海岸线资源管理中推广应用。
关键词:  无人机遥感  面向对象  半自动化  岸线提取
DOI:10.11759/hykx20200509001
分类号:
基金项目:海洋公益性科研专项(201405028-4)
Research on object-oriented method of extracting coastline from UAV remote sensing image
Ma Deming1, Liu Yanxiong1, Jin Yongde1, Cheng Baoquan2
1.The First Institute of Oceanography,Ministry of Natural Resources;2.Qingjian Group Co., Ltd
Abstract:
The coastline is one of the important signs of the coastal zone, and also the baseline and "starting line" of the marine development strategy. It not only marks the boundary between land and sea, but also contains rich environmental information, and has an important role in indicating the coastal beach utilization and the rise and fall of Wetland ecosystem. As a precious natural resource, coastline plays an important role in coastal economic development. How to quickly and accurately extract the information of coastline location and its dynamic changes is an urgent problem for natural resources authorities and researchers. In this paper, the coastline of Qingdao small Island Bay was taken as the research object, and the UAV remote sensing image was taken as the basic data. An object-oriented coastline extraction method was proposed. Combined with the field test, the application experiment of artificial coastline and sandy coastline identification was carried out. The results show that the probability edge index PRI of artificial coastline and sandy coastline are 0.97 and 0.88, respectively, and the edge positioning error BDE is 4.33 and 2.84, respectively. The extracted artificial coastline and sandy coastline are basically similar to the measured coastline results. There are only slight differences in local fineness, and the accuracy of coastline identification is high. It is proved that the method can quickly and effectively acquire the coastline information, and its accuracy can meet the needs of coastline dynamic change monitoring, and can be promoted and applied in coastline resource management.
Key words:  UAV remote sensing  object-oriented  semi-automatic  Coastline Extraction  dynamic change monitoring
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