Building digital elevation model with eroded microtopography from point cloud

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (36): 193-196.

• 图形、图像、模式识别 • Previous Articles Next Articles

Building digital elevation model with eroded microtopography from point cloud

LONG Mansheng1，XU Hongzhi1，ZHANG Daokun2，CHEN Tongju1

1.College of Information Engineering，Northwest A&F University，Yangling，Shaanxi 712100，China
2.College of Science，Northwest A&F University，Yangling，Shaanxi 712100，China

Received:1900-01-01 Revised:1900-01-01 Online:2011-12-21 Published:2011-12-21

侵蚀坡面数字高程模型重构算法研究

龙满生1，徐洪志1，张道坤2，陈同聚1

1.西北农林科技大学信息工程学院，陕西杨凌 712100
2.西北农林科技大学理学院，陕西杨凌 712100

Abstract

Abstract: Building grid Digital Elevation Model（DEM） with microtopography is important for hydraulic erosion simulation on hillslope.Performance of the constant distance neighbor interpolation is analyzed and a Delaunay triangulation with linear interpolation is proposed to build the eroded slope surface from point cloud.This algorithm consists of projection，triangulation，segmentation and interpolation stages.Principal component analysis is used to compute the major and minor axes of the point cloud and to align them with the coordinate axes.A Triangulated Irregular Network（TIN） mesh is created by the Delaunay triangulation within the bounding box of the point cloud.The grid DEM is reconstructed by the linear interpolation inside the Delaunay triangle after the spatial domain is segmented into uniform grids with specified resolution.Experimental results show that the performance is optimal for the constant distance neighbor interpolation when the ratio of the searching radius to the grid resolution is about 1.414，and that the Delaunay triangulation interpolation is better than the constant distance neighbor interpolation in reconstructing the grid DEM with microtopography.

Key words: point cloud processing, digital elevation model, principal component analysis, Delaunay triangulation

摘要： 构建具有微地貌的数字高程模型是坡面水蚀模拟的基础工作。针对定距邻域内插法的不足，提出了由点云构建侵蚀坡面的Delaunay三角内插法。该算法包括投影、三角化、分割和插值等步骤，利用主元分析法计算坡面点云的主轴和次轴并将其与坐标轴对齐，再由Delaunay三角化生成不规则三角网，将其分割为指定分辨率的规则格网，由三角线性插值得到格网单元的高程。试验结果表明，当邻域搜索半径与格网单元边长之比约为1.414时，定距邻域内插法的性能达到最优;当点云含有较多侵蚀坑等微地貌信息时，Delaunay三角内插法优于定距邻域内插法。

关键词: 点云处理, 数字高程模型, 主元分析, Delaunay三角化

LONG Mansheng1，XU Hongzhi1，ZHANG Daokun2，CHEN Tongju1. Building digital elevation model with eroded microtopography from point cloud[J]. Computer Engineering and Applications, 2011, 47(36): 193-196.

龙满生1，徐洪志1，张道坤2，陈同聚1. 侵蚀坡面数字高程模型重构算法研究[J]. 计算机工程与应用, 2011, 47(36): 193-196.

[1]	QIAO Hui, ZHOU Shuisheng. Nolinear Angle 2DPCA and Its Application on Face Recognition [J]. Computer Engineering and Applications, 2021, 57(8): 112-118.
[2]	YU Duo, HUANG Yongdong. Hyperspectral Image Classification Based on SPCA and Domain Transform Recursive Filtering [J]. Computer Engineering and Applications, 2021, 57(4): 199-208.
[3]	SHEN Shaoyu, CAI Manchun, LU Tianliang, ZHAO Qi. Intrusion Detection Algorithm based on LFKPCA-DWELM [J]. Computer Engineering and Applications, 2021, 57(17): 130-137.
[4]	ZHANG Dongmei, Mairidan Wushouer, Gulanbaier Tuerhong. One-Class Classification Method for High-Dimensional Mixed and Unbalanced Credit Score Data [J]. Computer Engineering and Applications, 2021, 57(10): 233-240.
[5]	LIN Kezheng, ZHANG Yuanming, LI Haotian. Research on HOG Feature Extraction Algorithm Weighted by Information Entropy [J]. Computer Engineering and Applications, 2020, 56(6): 147-152.
[6]	QIU Ningjia, WANG Xiaoxia, WANG Peng, ZHOU Sicheng, WANG Yanchun. Research on Convolutional Neural Network Algorithm Combined with Transfer Learning Model [J]. Computer Engineering and Applications, 2020, 56(5): 43-48.
[7]	HUANG Guangjun, DENG Yuanlong. Polarizer Visual Defect Detection and Classification Based on Improved LBP and SVM Algorithm [J]. Computer Engineering and Applications, 2020, 56(22): 251-255.
[8]	QIU Ningjia, SHEN Zhuorui, WANG Hui, WANG Peng. Semi-supervised Learning Optimization Algorithm for Communication Spam Text Recognition [J]. Computer Engineering and Applications, 2020, 56(17): 121-128.
[9]	YANG Yongpeng, YANG Zhenzhen, LI Jianlin, LE Jun. Low Rank and Sparse Decomposition and Its Application in Video and Image Processing [J]. Computer Engineering and Applications, 2020, 56(16): 21-30.
[10]	GENG Huantong, ZHOU Lifa, DING Yangyang, ZHOU Shansheng. Improved MOEA/D Algorithm Based on New Differential Evolution Model [J]. Computer Engineering and Applications, 2019, 55(8): 138-146.
[11]	CHEN Jia, LIU Dongxue, WU Dashuo. Stock Index Forecasting Method Based on Feature Selection and LSTM Model [J]. Computer Engineering and Applications, 2019, 55(6): 108-112.
[12]	NIU Yaxi, JI Xiaoping. Image Retrieval Algorithm Based on Convolutional Neural Network [J]. Computer Engineering and Applications, 2019, 55(18): 201-206.
[13]	XU Jingze, WU Zuohong, XU Yan, ZENG Jianhang. Face Recognition Based on PCA，LDA and SVM Algorithms [J]. Computer Engineering and Applications, 2019, 55(18): 34-37.
[14]	MA Yichao, ZHAO Yunji, ZHANG Xinliang. CNN Handwritten Digital Recognition Algorithm Based on PCA Initialization Convolution Kernel [J]. Computer Engineering and Applications, 2019, 55(13): 134-139.
[15]	YANG Baoshan1，2，3, HU Yegang1，2，3, ZHANG Jicong1，2，3. Application of Signal Detection of Epileptic Spike with Magnetoencephalogram Based on 2DPCA [J]. Computer Engineering and Applications, 2019, 55(1): 186-190.

Building digital elevation model with eroded microtopography from point cloud

侵蚀坡面数字高程模型重构算法研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics