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Automatic Detecting Outliers in Multibeam Sonar Based on Density of Points

机译:基于点密度自动检测多钻石声纳的异常值

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Because of device noises, bad sea state or incorrect ship parameter, multibeam bathymetry data easily conceal many outliers. In order to process such large amount of data, we must research an automatic and rapid and robust approach. We present an automatic algorithm for detecting outliers based on density of points. Firstly, each swath data are projected along orthogonal and side direction respectively. On each plane, an initial point can be determined according to the corresponding maximum density. Then a big region will be searched by the connected neighboring points on each plane. Then we adopt erosion and dilation algorithms to eliminate a few outliers which connected with the big region. Afterward we obtain the edge of region by edge tracing. All data beyond of the region will be considered as outliers and deleted. Finally a local window filter is used to delete some outliers which conceal in the scope of real depth. The algorithm is verified by real data. It is a kind of rapid, robust algorithm.
机译:由于设备噪音,海洋状态不当或船舶参数不正确,多次河流沐浴术数据很容易隐藏许多异常值。为了处理大量数据,我们必须研究自动和快速且强大的方法。我们提出了一种自动算法,用于检测基于点密度的异常值。首先,每个SWATH数据分别沿正交和侧向投射。在每个平面上,可以根据相应的最大密度确定初始点。然后,每个平面上的连接的相邻点将搜索一个大区域。然后我们采用腐蚀和扩张算法来消除与大区域相关的几个异常值。之后,我们通过边缘跟踪获得区域的边缘。该区域的所有数据将被视为异常值并删除。最后,本地窗口过滤器用于删除隐藏在真实深度范围内的一些异常值。该算法通过实际数据验证。它是一种快速,稳健的算法。

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