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面向多波束测深数据的双向布料模拟自动滤波算法
引用本文:杨安秀,吴自银,阳凡林,宿殿鹏,冯成凯,许方正.面向多波束测深数据的双向布料模拟自动滤波算法[J].武汉大学学报(信息科学版),2022,47(4):517-525.
作者姓名:杨安秀  吴自银  阳凡林  宿殿鹏  冯成凯  许方正
作者单位:1.山东科技大学测绘与空间信息学院,山东 青岛,266590
基金项目:国家自然科学基金52001189国家自然科学基金41830540国家自然科学基金41930535自然资源部海底科学重点实验室开放基金KLSG2106山东科技大学科研创新团队支持计划2019TDJH103自然资源部第二海洋研究所科研基金JZ1902"全球变化与海气相互作用"专项GASI-EOGE-01
摘    要:针对现有多波束测深数据的滤波算法需要人工干预且难以实现自动滤波的问题,在布料模拟滤波基础上,提出了一种基于双向布料模拟(bidirectional cloth simulation filtering, BCSF)的多波束测深数据滤波算法。首先,基于二次曲面(Levenberg-Marquardt)算法拟合构建传递式迭代趋势面,消除海底负异常数据;然后,构建BCSF修正模型,确定最终海底滤波面,解决海底凹凸地形或具有成簇噪点的复杂海域地形容易产生的过度滤波问题;最后,对分类海底点与非海底点的距离阈值进行了自适应优化与估计,进一步提高BCSF滤波结果的准确性。将所提算法应用于实测多波束测深数据,实验结果表明,与布料模拟滤波相比,所提算法不仅克服了过度滤波的缺陷,而且实验区域的整体测试数据的噪点剔除率从12.87%下降到0.76%,局部测试数据的噪点剔除率从15.29%下降到1.09%;与基于不确定度理论的多波束测深滤波相比,所提算法更加简洁,易于技术实现,人工干预很少,保留了更多的地形细节,具有较好的鲁棒性和应用前景。

关 键 词:多波束测深    双向布料模拟滤波    传递式迭代趋势面    自适应距离阈值    多波束测深滤波
收稿时间:2020-07-12

An Automatic Filtering Algorithm of Multi-beam Bathymetry Based on Bidirectional Cloth Simulation
Institution:1.College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China2.Key Laboratory of Ocean Geomatics, MNR, Qingdao 266590, China3.Key Laboratory of Submarine Geoscience, Second Institute of Oceanography, MNR, Hangzhou 310012, China
Abstract:  Objectives  To overcome the problem that the current bathymetric filtering methods require manual intervention and are difficult to implement technically, a bidirectional cloth simulation filtering (BCSF) algorithm is proposed and implemented in this paper.  Methods  Firstly, the transfer iterative trend surface is established to eliminate the negative anomalies and guarantee the continuous expression of the seafloor topography. Then, the filtering surface is established to solve the over-filtering problem of convex and concave seafloor topographies based on the proposed BCSF correction model. Finally, to further improve the effectiveness of the filtering, adaptive distance threshold is optimized and estimated. To evaluate the performance of the proposed algorithm, the BCSF algorithm is applied to shallow water multibeam bathymetry data.  Results  The experimental results show that the BCSF algorithm can avoid the over-filtering. The elimination rate of the proposed BCSF algorithm is better than that of the CSF (cloth simulation filtering) algorithm, which decreases from 12.87% to 0.76% for the whole study area and from 15.29% to 1.09% for local study area, respectively.  Conclusions  Compared with the CUBE (combined uncertainty bathymetry estimation) algorithm, the BCSF algorithm is more easily to implement and can retain more terrain details. Consequently, the BCSF algorithm has strong robustness and application prospects for multibeam bathymetry data.
Keywords:
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