ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 267-271, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-267-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Sep 2017
SEMI – GLOBAL MERGING OF DIGITAL SURFACE MODELS FROM MULTIPLE STEREOPAIRS
S. Pang1,2, X. Hu1,3, M. Zhang3, and L. Ye4 1Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
2School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
4School of Educational Information Technology, Central China Normal University, Wuhan 430079, China
Keywords: Digital surface model, Merging, Stereopairs, Semi – global optimization, Aerial images, Point cloud data Abstract. The semi-global optimization algorithm, which approximates a global 2D smoothness constraint by combining several 1D constraints, has been widely used in the field of image dense matching for digital surface model (DSM) generation. However, due to occlusion, shadow and textureless area of the matching images, some inconsistency may exist in the overlapping areas of different DSMs. To address this problem, based on the DSMs generated by semi-global matching from multiple stereopairs, a novel semi-global merging algorithm is proposed to generate a reliable and consistent DSM in this paper. Two datasets, each covering 1 km2, are used to validate the proposed method. Experimental results show that the optimal DSM after merging can effectively eliminate the inconsistency and reduce redundancy in the overlapping areas.
Conference paper (PDF, 1649 KB)


Citation: Pang, S., Hu, X., Zhang, M., and Ye, L.: SEMI – GLOBAL MERGING OF DIGITAL SURFACE MODELS FROM MULTIPLE STEREOPAIRS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 267-271, https://doi.org/10.5194/isprs-annals-IV-2-W4-267-2017, 2017.

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