面向全球DEM生产的点云智能滤波与DEM泊松编辑方法
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摘要
国家全球战略急需全球地理信息资源的支撑,如何利用国产高分辨率卫星影像高效生产全球DEM已经成为我国全球地理信息资源建设工程的重大任务。由于全球地形地表结构的多样性和复杂性,现有依靠单一滤波模型或有限滤波规则的点云滤波方法的可靠性和效率难以保证。为此,本文提出一种可靠、高效、稳健的影像密集匹配点云数据智能滤波与DEM泊松编辑方法,通过顾及弯曲能量的点云自适应滤波及多边界约束的泊松地形编辑方法的设计与实现,构建了点云自适应滤波与定向智能精准编辑软件LINK。通过四川、黑龙江、陕西、海南、重庆测绘地理信息局等多家生产单位,采用覆盖国内外重点区域不同地形地表结构特征的资源三号卫星影像的DSM数据,进行DEM试生产验证。结果证明了本文方法的可靠性和有效性,在DEM生产困难的建筑区、森林和水域等区域,精度和效率优势明显,为全球大规模DEM生产提供了有力支撑。
Precision and high-resolution global DEM composes the fundamental basis of the spatial data infrastructure for a variety of applications. To obtain high precision global DEM using domestic high-resolution satellite images, is one of the most important requirements for the construction of global geographic information resources of China. However, due to the complexity of the structure of terrain surface, a single set of parameters is not capable to handle the intricacies of the ground characteristics, which leads to decreasing of accuracies. Aiming at solving this problem, this paper proposes an adaptive surface filter for automatic terrain filtering and Poisson terrain editing for post processing and quality control. The two contributions have been extended to the software framework, LINK, for the global DEM production. Experiments and production works by several departments, including the Sichuan, Heilongjiang, Shanxi, Hainan, Chongqing Bureau of Surveying, Mapping and Geoinformation, have revealed that the DEM quality satisfies with the standard set, including various difficult scenarios, such as building high-rise areas, forest and water body. The quality and efficiency of the proposed method exceed other competitive solutions, which indicates a strong potential for the on-going global DEM production of China.
Precision and high-resolution global DEM composes the fundamental basis of the spatial data infrastructure for a variety of applications. To obtain high precision global DEM using domestic high-resolution satellite images, is one of the most important requirements for the construction of global geographic information resources of China. However, due to the complexity of the structure of terrain surface, a single set of parameters is not capable to handle the intricacies of the ground characteristics, which leads to decreasing of accuracies. Aiming at solving this problem, this paper proposes an adaptive surface filter for automatic terrain filtering and Poisson terrain editing for post processing and quality control. The two contributions have been extended to the software framework, LINK, for the global DEM production. Experiments and production works by several departments, including the Sichuan, Heilongjiang, Shanxi, Hainan, Chongqing Bureau of Surveying, Mapping and Geoinformation, have revealed that the DEM quality satisfies with the standard set, including various difficult scenarios, such as building high-rise areas, forest and water body. The quality and efficiency of the proposed method exceed other competitive solutions, which indicates a strong potential for the on-going global DEM production of China.
引文
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[9] PINGEL T J, CLARKE K C, MCBRIDE W A. An improved simple morphological filter for the terrain classification of airborne LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013(77): 21-30.
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[11] WANG Cheng, GLENN N F. Integrating LiDAR intensity and elevation data for terrain characterization in a forested area[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(3): 463-466.
[12] ZHU Qing, LI Yuan, HU Han, et al. Robust point cloud classification based on multi-level semantic relationships for urban scenes[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017(129): 86-102.
[13] HU Han, DING Yulin, ZHU Qing, et al. An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014(92): 98-111.
[14] SZELISKI R. Computer vision: algorithms and applications[M]. London: Springer, 2011: 812.
[15] BOOKSTEIN F L. Principal warps: Thin-plate splines and the decomposition of deformation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(6): 567-585.
[16] MORSE B S, YOO T S, RHEINGANS P, et al. Interpolating implicit surfaces from scattered surface data using compactly supported radial basis functions[C]//Proceedings of the International Conference on Shape Modeling & Applications. Washington, DC: IEEE Computer Society, 2001.
[17] PéREZ P, GANGNET M, BLAKE A. Poisson image editing[C]//SIGGRAPH ’03 ACM SIGGRAPH 2003 Papers. San Diego, California: ACM, 2003.
[18] FARBMAN Z, FATTAL R, LISCHINSKI D. Convolution pyramids[J]. ACM Transactions on Graphics (TOG), 2011, 30(6): 175.
[19] WAECHTER M, MOEHRLE N, GOESELE M. Let there be color! large-scale texturing of 3D reconstructions[M]//FLEET D, PAJDLA T, SCHIELE B, et al. Computer Vision-ECCV 2014. Cham: Springer, 2014: 836-850.
[20] KAZHDAN M, BOLITHO M, HOPPE H. Poisson surface reconstruction[C]//Symposium on Geometry Processing. [S.l.]: The Eurographics Association, 2006.
[2] 李德仁. 我国第一颗民用三线阵立体测图卫星——资源三号测绘卫星[J]. 测绘学报, 2012, 41(3): 317-322. LI Deren. China’s first civilian three-line-array stereo mapping satellite: ZY-3[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(3): 317-322.
[3] 唐新明, 张过, 祝小勇, 等. 资源三号测绘卫星三线阵成像几何模型构建与精度初步验证[J]. 测绘学报, 2012, 41(2): 191-198. TANG Xinming, ZHANG Guo, ZHU Xiaoyong, et al. Triple linear-array imaging geometry model of Ziyuan-3 surveying satellite and its validation[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2): 191-198.
[4] KRIEGER G, MOREIRA A, FIEDLER H, et al. TanDEM-X: a satellite formation for high-resolution SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(11): 3317-3341.
[5] RIEGLER G, HENNIG S D, WEBER M. WorldDEM-A novel global foundation layer[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015, 40(3): 183-187.
[6] 王密, 杨博, 李德仁, 等. 资源三号全国无控制整体区域网平差关键技术及应用[J]. 武汉大学学报(信息科学版), 2017, 42(4): 427-433. WANG Mi, YANG Bo, LI Deren, et al. Technologies and applications of block adjustment without control for ZY-3 images covering China[J]. Geomatics and Information Science of Wuhan University, 2017, 42(4): 427-433.
[7] VOSSELMAN G. Automated planimetric quality control in high accuracy airborne laser scanning surveys[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012(74): 90-100.
[8] XU S, VOSSELMAN G, ELBERINK S O. Multiple-entity based classification of airborne laser scanning data in urban areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014(88): 1-15.
[9] PINGEL T J, CLARKE K C, MCBRIDE W A. An improved simple morphological filter for the terrain classification of airborne LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013(77): 21-30.
[10] SITHOLE G, VOSSELMAN G. Experimental comparison of filter algorithms for bare-earth extraction from airborne laser scanning point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2004, 59(1-2): 85-101.
[11] WANG Cheng, GLENN N F. Integrating LiDAR intensity and elevation data for terrain characterization in a forested area[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(3): 463-466.
[12] ZHU Qing, LI Yuan, HU Han, et al. Robust point cloud classification based on multi-level semantic relationships for urban scenes[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017(129): 86-102.
[13] HU Han, DING Yulin, ZHU Qing, et al. An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014(92): 98-111.
[14] SZELISKI R. Computer vision: algorithms and applications[M]. London: Springer, 2011: 812.
[15] BOOKSTEIN F L. Principal warps: Thin-plate splines and the decomposition of deformation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(6): 567-585.
[16] MORSE B S, YOO T S, RHEINGANS P, et al. Interpolating implicit surfaces from scattered surface data using compactly supported radial basis functions[C]//Proceedings of the International Conference on Shape Modeling & Applications. Washington, DC: IEEE Computer Society, 2001.
[17] PéREZ P, GANGNET M, BLAKE A. Poisson image editing[C]//SIGGRAPH ’03 ACM SIGGRAPH 2003 Papers. San Diego, California: ACM, 2003.
[18] FARBMAN Z, FATTAL R, LISCHINSKI D. Convolution pyramids[J]. ACM Transactions on Graphics (TOG), 2011, 30(6): 175.
[19] WAECHTER M, MOEHRLE N, GOESELE M. Let there be color! large-scale texturing of 3D reconstructions[M]//FLEET D, PAJDLA T, SCHIELE B, et al. Computer Vision-ECCV 2014. Cham: Springer, 2014: 836-850.
[20] KAZHDAN M, BOLITHO M, HOPPE H. Poisson surface reconstruction[C]//Symposium on Geometry Processing. [S.l.]: The Eurographics Association, 2006.
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