基于dem的黄土肩线提取优化区域增长算法

Zihan Liu, Hongming Zhang, Liang Dong, Zhixuan Sun, Shufang Wu, Biao Zhang, Lin-shan Yuan, Zhenfei Wang, Qimeng Jia
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摘要

黄土高原的正负地形(P-N地形)是衡量地表侵蚀程度和区分地貌类型的重要地理地形要素。黄土肩线是黄土高原重要的地形特征,常被用作判别土壤磷氮含量的判据。肩线的提取对于预测侵蚀和识别沟头非常重要。但是,对于坡度不明显地区的黄土肩线提取,现有算法还有待改进。本研究提出了一种区域融合(RF)方法,将基于坡度变化的方法与区域生长算法相结合,在空间分辨率为5 m的数字高程模型(DEM)上提取黄土肩线。该方法在区域生长算法的生长标准中引入不同的地形因素,提取黄土肩线。首先,采用基于坡度变化的方法构建黄土肩线初始集,并利用光滑dem与真实dem的差值提取N个地形的初始集。其次,采用改进生长标准的区域生长算法,对黄土肩线和N地形候选区域生成一个完整的区域,融合生成和整合等高线,消除不连续性;最后,通过检测综合轮廓的边缘来识别黄土肩线,结果显示聚集点或马刺,通过命中或不命中变换消除以优化最终结果。对陕西黄土丘岭试验区的验证表明,基于欧几里得距离偏移百分比的RF方法在10 m偏差范围内的精度达到了人工数字化方法的96.9%。基于平均绝对误差和标准绝对偏差值,与Zhou改进的蛇形模型和双向DEM地形-遮阳方法相比,提出的RF方法提取黄土肩线的精度较高。
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An Optimised Region-Growing Algorithm for Extraction of the Loess Shoulder-Line from DEMs
The positive and negative terrains (P–N terrains) of the Loess Plateau of China are important geographical topography elements for measuring the degree of surface erosion and distinguishing the types of landforms. Loess shoulder-lines are an important terrain feature in the Loess Plateau and are often used as a criterion for distinguishing P–N terrains. The extraction of shoulder lines is important for predicting erosion and recognising a gully head. However, existing extraction algorithms for loess shoulder-lines in areas with insignificant slopes need to be improved. This study proposes a regional fusion (RF) method that integrates the slope variation-based method and region-growing algorithm to extract loess shoulder-lines based on a Digital Elevation Model (DEM) at a spatial resolution of 5 m. The RF method introduces different terrain factors into the growth standards of the region-growing algorithm to extract loess-shoulder lines. First, we employed a slope-variation-based method to build the initial set of loess shoulder-lines and used the difference between the smoothed and real DEMs to extract the initial set for the N terrain. Second, the region-growing algorithm with improved growth standards was used to generate a complete area of the candidate region of the loess shoulder-lines and the N terrain, which were fused to generate and integrate contours to eliminate the discontinuity. Finally, loess shoulder-lines were identified by detecting the edge of the integrated contour, with results exhibiting congregate points or spurs, eliminated via a hit-or-miss transform to optimise the final results. Validation of the experimental area of loess ridges and hills in Shaanxi Province showed that the accuracy of the RF method based on the Euclidean distance offset percentage within a 10-m deviation range reached 96.9% compared to the manual digitalisation method. Based on the mean absolute error and standard absolute deviation values, compared with Zhou’s improved snake model and the bidirectional DEM relief-shading methods, the proposed RF method extracted the loess shoulder-lines highly accurately.
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