Ground Points Recognition and DEM Extraction Based on DSM in Complex Terrain

doi:10.13474/j.cnki.11-2246.2018.0144

Abstract

Abstract:

At present,researches of the extraction of DEM focused on DSM are mostly based on the flat areas,and the precision of extracting DEM is low.Therefore,in this article,a DEM extraction algorithm based on regional growth is proposed and implements ground points recognition and DEM extraction form DSM in complex terrain.The algorithm uses the maximum interclass variance (OTSU) to realize the adaptive selection of seed points,and growth criterion,and termination condition in regional growth.This method identifies the elevated road from the city DSM data to avoid that the regional growth algorithm wrongly divides the elevated road into the ground because of the continuity between the elevated road and the ground.By comparing with the method of local minimum,the results show that the proposed algorithm can improve the accuracy of DEM extraction,and the mapping precision of ground points recognition exceeds 90%,and experimental results demonstrate that the effectiveness of the proposed method is reliable and steady.

Key words: DSM, DEM, regional growth, complex terrain, elevated road

CLC Number:

YU Longhua, WANG Leiguang, WU Nan, LIAO Shengxi, ZHANG Huaiqing. Ground Points Recognition and DEM Extraction Based on DSM in Complex Terrain[J]. 测绘通报, 2018, 0(5): 59-64.

References

[1] 雷蓉,邱振戈,张士涛.基于遥感影像生成DEM的质量检查[J].测绘通报,2005(4):36-39.
[2] 汤博麟,蔡海峰,牟玉兴.DEM制作检查及精度问题的解决方法[J].地理空间信息,2016,10(6):76-78.
[3] 马红.利用高精度DSM数据提取建筑物轮廓算法研究[J].测绘通报,2015(4):111-124.
[4] 李忠,李梅,杜绪奎.平坦地区DSM到DEM的试验[J].测绘与空间地理信息,2008,31(2):29-31.
[5] ISMAIL Z,JAAFAR J.DEM Derived from Photogram-metric Generated DSM Using Morphological Filter[C]//Control and System Graduate Research Colloquium.[S.l.]:IEEE,2013.
[6] 徐海荣,田联房,陈萍,等.改进的区域生长算法在医学图像分割中的应用[J].生物医学工程研究,2005,24(3):187-190.
[7] 林瑶,田捷.医学图像分割方法综述[J].模式识别与人工智能,2002,15(2):192-204.
[8] KAMDI S,KRISHNA R K.Image Segmentation and Region Growing Algorithm[J].International Journal of Computer Technology & Electronics Engineering,2012,1(2):103-107.
[9] POHLE R,TOENNIES KD.A New Approach for Model-based Adaptive Region Growing in Medical Image Analysis[M].Berlin Heidelberg:Springer,2001:238-246.
[10] LIN Z,JIN J,TALBOT H.Unseeded Region Growing for 3D Image Segmentation[J].Journal of Trauma,2000,24(11):938-945.
[11] 朱绍攀,张书毕.分形地形复杂度研究[J].地理空间信息,2011,9(3):116-119.
[12] 苏明超,郭书军.基于区域生长算法的CT图像肺组织分割[C]//中国电子学会全国网络编码学术年会论文集.北京:国防工业出版社,2008.
[13] 刘怡君,庞勇,廖声熙,等.机载LiDAR和高光谱融合实现普洱山区树种分类[J].林业科学研究,2016,29(3):407-412.
[14] LIAO W,PIŽURICA A,BELLENS R,et al.Generalized Graph-based Fusion of Hyperspectral and LiDAR Data Using Morphological Features[J].IEEE Geoscience & Remote Sensing Letters,2015,12(3):552-556.
[15] 董彦芳,庞勇,许丽娜,等.高光谱遥感影像与机载LiDAR数据融合的地物提取方法研究[J].遥感信息,2014(6):73-76.
[16] 彭仪普,刘文熙.实时交互的多分辨率地形模型[J].武汉大学学报(信息科学版),2003,28(4):480-483.