| 基于多源数据和船舶停留轨迹语义建模的港口目标识别 |
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| 引用本文： | 闫兆进,杨慧.基于多源数据和船舶停留轨迹语义建模的港口目标识别[J].地球信息科学,2022,24(9):1662-1675. |
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| 作者姓名： | 闫兆进 杨慧 |
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| 作者单位： | 1.中国矿业大学 资源与地球科学学院, 徐州 2211162.中国矿业大学 煤层气资源与成藏过程教育部重点实验室, 徐州 2211163.中国矿业大学 人工智能研究院, 徐州 221116 |
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| 基金项目： | 中央高校基本业务费(2022-11242);国家自然科学基金项目(41971335);国家自然科学基金项目(51978144);江苏高校优势学科建设工程资助项目。 |
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| 摘 要： | 港口目标识别是海事船舶监管的重中之重,船舶自动识别系统（Automatic Identification System,AIS）所获取的船舶活动信息,可为港口目标识别提供高时相和高精度的船舶航行数据。为了探究AIS数据在港口目标识别中的应用,提出一种基于多源数据和船舶停留轨迹语义建模的港口目标识别方法。通过数据挖掘和语义信息增强构建船舶停留轨迹语义模型,识别船舶港口停留轨迹;建立基于随机森林的船舶停留方式分类模型,分类船舶泊位停留轨迹和船舶锚地停留轨迹,并利用空间逐级合并方法提取港口泊位和港口锚地;综合船舶泊位停留轨迹、道路、海岸线、水深、土地利用与土地覆盖等数据,顾及情景-领域知识实现港口目标识别。基于2017年96 790艘船舶的超8300万条AIS轨迹记录,应用本文方法识别南海研究区的港口目标。实验结果表明,本文方法对于船舶轨迹停留行为总体分类精度为0.9477, Kappa系数为0.8948。提取出南海研究区447个港口区域,与Google Earth影像叠加验证结果表明,提取结果均位于真实的港口影像内,相较于Natural Earth数据集中包含的南海区域24个港口点位,提取结果的完整性大大增强。因此,基于多源数据和船舶停留语义建模的港口目标识别方法对于港口目标识别具有较高的准确性和完整性。此外,该方法提取的港口区域可为基于遥感影像的港口目标识别提供靶区,从而提高大区域甚至全球范围内港口目标动态识别的效率。
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| 关 键 词： | 港口识别 船舶行为分类 船舶自动识别系统 多源数据 语义模型 船舶停留 轨迹挖掘 |
| 收稿时间： | 2021-08-21 |
Harbor Detection based on Multi-Source Data and Semantic Modeling of Ship Stop Trajectory |
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| YAN Zhaojin,YANG Hui.Harbor Detection based on Multi-Source Data and Semantic Modeling of Ship Stop Trajectory[J].Geo-information Science,2022,24(9):1662-1675. |
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| Authors: | YAN Zhaojin YANG Hui |
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| Institution: | 1. School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China2. Key Laboratory of Coal Bed Gas Resources and Forming Process of Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China3. Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China |
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| Abstract: | Harbor detection is the top priority of maritime ship supervision, and the ship activity information acquired by Automatic Identification System (AIS) can provide high temporal and spatial accuracy of ship activity information for harbor detection. In order to explore the application of AIS data in harbor detection, a harbor detection method based on multi-source data and semantic modeling of ship stop trajectory is proposed. Firstly, the semantic model of ship stop trajectory is constructed through data mining and semantic information enhancement to identify ship stop trajectory in the harbor area. Secondly, a classification model based on random forest is established to classify ship berthing trajectories and ship anchoring trajectories, and then harbor berths and anchorages are extracted by using spatial step-by-step merging method. Finally, the data of ship berthing trajectories, roads, coastline, bathymetry, and land use and land cover data are integrated to identify harbor objects considering situational-domain knowledge. Based on over 83 million AIS trajectory records of 96,790 ships in 2017, the proposed method is applied to detect harbor object in the South China Sea study area. The experimental results show that the overall classification accuracy of ship stop behavior is 0.9477 and the Kappa coefficient is 0.8948. 447 harbor areas in the South China Sea study area are extracted, and the overlay verification results with Google Earth images show that the extraction results are all located within the real harbor images. In addition, compared with the 24 harbor locations in the South China Sea region contained in the Natural Earth dataset, the integrity of the extraction results is greatly enhanced. Therefore, the harbor detection method based on multi-source data and semantic modeling of ship stop trajectory has high accuracy and completeness for harbor detection. Meanwhile, the harbor areas extracted by this method can provide target areas for harbor identification based on remote sensing images, thus improving the efficiency of dynamic identification of harbor object in a large region or even globally. |
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| Keywords: | harbor detection ship activity Automatic Identification System (AIS) multi-source data semantic model ship stop trajectory mining |
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