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A New Extraction Method of Loess Shoulder-Line Based on Marr-Hildreth Operator and Terrain Mask

机译:基于Marr-Hildreth算子和地形遮罩的黄土肩线提取新方法

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摘要

Loess shoulder-lines are significant structural lines which divide the complicated loess landform into loess interfluves and gully-slope lands. Existing extraction algorithms for shoulder-lines mainly are based on local maximum of terrain features. These algorithms are sensitive to noise for complicated loess surface and the extraction parameters are difficult to be determined, making the extraction results usually inaccurate. This paper presents a new extraction approach for loess shoulder-lines, in which Marr-Hildreth edge operator is employed to construct initial shoulder-lines. Then the terrain mask for confining the boundary of shoulder-lines is proposed based on slope degree classification and morphology methods, avoiding interference from non-valley area and modify the initial loess shoulder-lines. A case study is conducted in Yijun located in the northern Shanxi Loess Plateau of China. The Digital Elevation Models with a grid size of 5 m is applied as original data. To obtain optimal scale parameters, the Euclidean Distance Offset Percentages between shoulder-lines is calculated by the Marr-Hildreth operator and the manual delineations. The experimental results show that the new method could achieve the highest extraction accuracy when σ = 5 in Gaussian smoothing. According to the accuracy assessment, the average extraction accuracy is about 88.5%, which indicates that the proposed method is applicable for the extraction of loess shoulder-lines in the loess hilly and gully areas.
机译:黄土路肩线是重要的构造线,将复杂的黄土地貌分为黄土汇流处和沟壑坡地。现有的用于肩线的提取算法主要基于地形特征的局部最大值。这些算法对复杂的黄土表面噪声敏感,提取参数难以确定,提取结果通常不准确。本文提出了一种新的黄土肩线提取方法,其中采用Marr-Hildreth边缘算子构造初始肩线。然后基于坡度分类和形态学方法,提出了一种限定肩线边界的地形掩膜,避免了非谷区的干扰,并对黄土的初始肩线进行了修改。在中国山西黄土高原北部的宜君市进行了案例研究。网格大小为5 m的数字高程模型被用作原始数据。为了获得最佳的比例参数,肩线之间的欧几里得距离偏移百分比是由Marr-Hildreth运算符和手动轮廓计算得出的。实验结果表明,在高斯平滑中,当σ= 5时,新方法可以获得最高的提取精度。根据精度评估,平均提取精度约为88.5%,表明该方法适用于黄土丘陵沟壑区黄土肩线的提取。

著录项

  • 期刊名称 other
  • 作者

    Sheng Jiang; Guoan Tang; Kai Liu;

  • 作者单位
  • 年(卷),期 -1(10),4
  • 年度 -1
  • 页码 e0123804
  • 总页数 12
  • 原文格式 PDF
  • 正文语种
  • 中图分类
  • 关键词

  • 入库时间 2022-08-21 11:15:57

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