Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 543-548, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
09 Jun 2016
Wei Yuan1,2, Shiyu Chen1, Yong Zhang1, Jianya Gong1,3, and Ryosuke Shibasaki2 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan
3Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China
Keywords: Dense Matching, Mean-SIFT, Optical Flow Interpolation, Multi-Constraints, RANSAC Abstract. Dense matching plays an important role in many fields, such as DEM (digital evaluation model) producing, robot navigation and 3D environment reconstruction. Traditional approaches may meet the demand of accuracy. But the calculation time and out puts density is hardly be accepted. Focus on the matching efficiency and complex terrain surface matching feasibility an aerial image dense matching method based on optical flow field is proposed in this paper. First, some high accurate and uniformed control points are extracted by using the feature based matching method. Then the optical flow is calculated by using these control points, so as to determine the similar region between two images. Second, the optical flow field is interpolated by using the multi-level B-spline interpolation in the similar region and accomplished the pixel by pixel coarse matching. Final, the results related to the coarse matching refinement based on the combined constraint, which recognizes the same points between images. The experimental results have shown that our method can achieve per-pixel dense matching points, the matching accuracy achieves sub-pixel level, and fully meet the three-dimensional reconstruction and automatic generation of DSM-intensive matching’s requirements. The comparison experiments demonstrated that our approach’s matching efficiency is higher than semi-global matching (SGM) and Patch-based multi-view stereo matching (PMVS) which verifies the feasibility and effectiveness of the algorithm.
Conference paper (PDF, 1167 KB)

Citation: Yuan, W., Chen, S., Zhang, Y., Gong, J., and Shibasaki, R.: AN AERIAL-IMAGE DENSE MATCHING APPROACH BASED ON OPTICAL FLOW FIELD, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 543-548,, 2016.

BibTeX EndNote Reference Manager XML