Digital surface model refining using projected images

doi:10.13474/j.cnki.11-2246.2020.0178

Abstract

Abstract: With the wide use of photogrammetry technology and the increasing demand for photogrammetric products, true orthoimages (orthoimages generated by using digital surface models (DSM)) play an increasingly important role in many engineering applications and lives. How to obtain a highly accurate DSM is the key for true orthoimage generation. The current means of obtaining DSM is mainly through either digital photogrammetry technology or 3D laser scanning technology. For many reasons, the DSM obtained has more or less various error sources. The common methods to correct those DSM errors (DSM obtained through digital photogrammetry technology) are mainly manual editing or reprocessing the troubled images. These methods are time-consuming or/and labor intensive, low-accuracy and random. In this paper, a new method is proposed to correct some DSM errors to improve the quality and accuracy of DSM. The new method is based on the concepts of projected images and the projected loci of a spatial point on the projected images as the geometry constraint, employs some popular image matching techniques to find the correct elevations. The details of the method are given and results from two experiments demonstrate that the proposed method is effective and very promising in correcting and refining DSM, and has certain potentials in practice use.

Key words: DSM, orthoimage, projected image, projected locus, image matching

CLC Number:

P237

CHEN Yannan, WANG Jiali. Digital surface model refining using projected images[J]. Bulletin of Surveying and Mapping, 2020, 0(6): 45-48,56.

References

[1] HAALA N, ROTHERMEL M. Dense multiple stereo matching of highly overlapping UAV imagery[C]//International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Melbourne:XXII ISPRS Congress, 2012:387-392.
[2] 张剑清,潘励,王树根. 摄影测量学[M]. 武汉:武汉大学出版社,2003.
[3] AKEL N A, KREMEIKE K, FILIN S, et al. Dense DTM generalization aided by roads extracted from LiDAR data[C]//Proceedings of the ISPRS Workshop "Laser scanning 2005". Enschede:[s.n.], 2005:54-59.
[4] 李莹,苏国中,林宗坚. 倾斜影像中提取高精度DEM的方法研究[J]. 测绘通报,2017(2):30-34.
[5] 朱庆,陈崇泰,胡翰,等. 顾及纹理特征的航空影像自适应密集匹配方法[J]. 测绘学报,2017,46(1):62-72.
[6] 闫利,费亮,陈长海,等. 利用网络图进行高分辨率航空多视影像密集匹配[J]. 测绘学报,2016,45(10):1171-1181.
[7] 袁修孝,朱宜萱. 单站点无地面控制摄影机自检校[J]. 武汉大学学报(信息科学版),1987,12(4):34-45.
[8] REITINGER B, SORMANN M, ZEBEDIN L, et al. UltraMap V3-A revolution in aerial photogrammetry[C]//International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Melboume:XXII ISPRS Congress, 2012:149-152.
[9] 张伟阁,杨辽,曹良中,等. 基于无人机航空影像的真正射影像制作方法研究[R]. 上海:上海遥感与社会发展,2013:124-136.
[10] CHEN Y C, TSENG Y H, HSIEH C Y, et al. Object-space multi-image matching of mobile-mapping-system image sequences[C]//International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.[S.l.]:isprsarchive-XXXIX-B5, 2012:465-470.
[11] PCNG L. Utilization of existing elevation data base to improve correlation on kern DSR-11 analytical plotter[C]//International Archives of Photogrammetry and Remote Sensing.[S.l.]:isprsarchives congress, 1988:466-475.
[12] 张祖勋,张剑清. 数字摄影测量学[M]. 武汉:武汉大学出版社,2002.
[13] OHTA Y, KANADE T. Stereo by Intra-and Inter-Scanline Search Using Dynamic Programming[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1985, 7(2):139-154.
[14] GEMAN S, GEMAN D. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(6):721-741.
[15] HIRSCHMVLLER H. Stereo processing by semiglobal matching and mutual information[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2):328-341.