数字水深模型(Digital Bathymetric Models,简称“DBMs”),是近海工程建设、资源开发、环境保护等领域的重要基础地理信息数据。现有全球公开DBMs产品如GEBCO(The General Bathymetric Chart of the Oceans)、SRTM(The Shuttle Radar To...数字水深模型(Digital Bathymetric Models,简称“DBMs”),是近海工程建设、资源开发、环境保护等领域的重要基础地理信息数据。现有全球公开DBMs产品如GEBCO(The General Bathymetric Chart of the Oceans)、SRTM(The Shuttle Radar Topography Mission)、ETOPO(Earth Topography)等在不同海域的数据类型、数据来源和产品精度均存在差异。为利用全球测深数据和DBMs产品重建中国近海水深模型,本文提出一种基于水深分区的加权融合重建框架。首先,从5个维度(整体精度、不同水深、航线剖面、地理分区、局部细节)对比分析6种常用DBMs产品的可靠性和适用性;然后,顾及水深和地形特征对研究区进行分割和分区,并选取分区内最优DBMs产品,以最小误差为约束进行最优加权融合;最后,对融合结果进行实测值恢复、平滑滤波等后处理,形成中国海岸线周边近海海域15″分辨率高精度无缝水深模型。结果表明,融合结果相比SRTM30_PLUS、GEBCO_2022、SRTM15_V2.5.5和ETOPO_2022均方根误差降低了27%、14%、14%和13%,地形细节也得到保留,证明了该融合框架的可行性,可为多数据集大规模海底地形的融合重建和及时更新提供参考。展开更多
美国冰、云和陆地高程二号卫星(The Ice,Cloud,and Land Elevation Satellite-2,ICESat-2)是ICESat卫星的继任者,旨在监测地球的冰盖、冰川、海洋和陆地高程的变化等,其携带的地形激光高度计系统(ATLAS)发射532 nm波长的激光,具备一定...美国冰、云和陆地高程二号卫星(The Ice,Cloud,and Land Elevation Satellite-2,ICESat-2)是ICESat卫星的继任者,旨在监测地球的冰盖、冰川、海洋和陆地高程的变化等,其携带的地形激光高度计系统(ATLAS)发射532 nm波长的激光,具备一定的水体穿透能力。作为光子计数式激光雷达,ICESat-2的数据易受外界环境影响而接收到大量噪声光子,导致光子数据密度分布不均匀。本文提出了一种基于密度峰值聚类(Density Peak Clustering,DPC)算法的光子去噪方法,通过数据集的欧式距离计算局部密度作为点云数据的属性,采用基尼指数自适应选择最优截断距离,分别对日间和夜间数据进行多次实验,得出了两类数据的局部密度阈值参数。本文选取三处实验区域进行信号光子去噪分析,使用本文方法的去噪精度F值优于官方置信度标签去噪和传统密度聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN),可以应用于星载激光雷达数据去噪处理。最后,对去噪后的华光礁区域信号光子进行折射校正,与收集的DEM数据进行对比可见,结合本文去噪方法可以使用ICESat-2数据进行浅水域的水深测量。展开更多
Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the...Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R~2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R^(2) values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.展开更多
文摘数字水深模型(Digital Bathymetric Models,简称“DBMs”),是近海工程建设、资源开发、环境保护等领域的重要基础地理信息数据。现有全球公开DBMs产品如GEBCO(The General Bathymetric Chart of the Oceans)、SRTM(The Shuttle Radar Topography Mission)、ETOPO(Earth Topography)等在不同海域的数据类型、数据来源和产品精度均存在差异。为利用全球测深数据和DBMs产品重建中国近海水深模型,本文提出一种基于水深分区的加权融合重建框架。首先,从5个维度(整体精度、不同水深、航线剖面、地理分区、局部细节)对比分析6种常用DBMs产品的可靠性和适用性;然后,顾及水深和地形特征对研究区进行分割和分区,并选取分区内最优DBMs产品,以最小误差为约束进行最优加权融合;最后,对融合结果进行实测值恢复、平滑滤波等后处理,形成中国海岸线周边近海海域15″分辨率高精度无缝水深模型。结果表明,融合结果相比SRTM30_PLUS、GEBCO_2022、SRTM15_V2.5.5和ETOPO_2022均方根误差降低了27%、14%、14%和13%,地形细节也得到保留,证明了该融合框架的可行性,可为多数据集大规模海底地形的融合重建和及时更新提供参考。
文摘美国冰、云和陆地高程二号卫星(The Ice,Cloud,and Land Elevation Satellite-2,ICESat-2)是ICESat卫星的继任者,旨在监测地球的冰盖、冰川、海洋和陆地高程的变化等,其携带的地形激光高度计系统(ATLAS)发射532 nm波长的激光,具备一定的水体穿透能力。作为光子计数式激光雷达,ICESat-2的数据易受外界环境影响而接收到大量噪声光子,导致光子数据密度分布不均匀。本文提出了一种基于密度峰值聚类(Density Peak Clustering,DPC)算法的光子去噪方法,通过数据集的欧式距离计算局部密度作为点云数据的属性,采用基尼指数自适应选择最优截断距离,分别对日间和夜间数据进行多次实验,得出了两类数据的局部密度阈值参数。本文选取三处实验区域进行信号光子去噪分析,使用本文方法的去噪精度F值优于官方置信度标签去噪和传统密度聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN),可以应用于星载激光雷达数据去噪处理。最后,对去噪后的华光礁区域信号光子进行折射校正,与收集的DEM数据进行对比可见,结合本文去噪方法可以使用ICESat-2数据进行浅水域的水深测量。
基金The National Key R&D Program of China under contract Nos 2022YFC3003800,2020YFC1521700 and 2020YFC1521705the National Natural Science Foundation of China under contract No.41830540+3 种基金the Open Fund of the East China Coastal Field Scientific Observation and Research Station of the Ministry of Natural Resources under contract No.OR-SECCZ2022104the Deep Blue Project of Shanghai Jiao Tong University under contract No.SL2020ZD204the Special Funding Project for the Basic Scientific Research Operation Expenses of the Central Government-Level Research Institutes of Public Interest of China under contract No.SZ2102the Zhejiang Provincial Project under contract No.330000210130313013006。
文摘Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R~2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R^(2) values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.
