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Remote Sens. 2016, 8(2), 89; doi:10.3390/rs8020089

Multi-View Stereo Matching Based on Self-Adaptive Patch and Image Grouping for Multiple Unmanned Aerial Vehicle Imagery

1,2,* , 1,2,* , 1,†
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Institute of Hydroecology, Ministry of Water Resource and Chinese Academy of Sciences, Wuhan 430079, China
These authors contributed equally to this work.
Authors to whom correspondence should be addressed.
Academic Editors: Diego Gonzalez-Aguilera, Fabio Remondino, Norman Kerle and Prasad S. Thenkabail
Received: 26 November 2015 / Revised: 13 January 2016 / Accepted: 18 January 2016 / Published: 23 January 2016
View Full-Text   |   Download PDF [19213 KB, uploaded 23 January 2016]   |  


Robust and rapid image dense matching is the key to large-scale three-dimensional (3D) reconstruction for multiple Unmanned Aerial Vehicle (UAV) images. However, the following problems must be addressed: (1) the amount of UAV image data is very large, but ordinary computer memory is limited; (2) the patch-based multi-view stereo-matching algorithm (PMVS) does not work well for narrow-baseline cases, and its computing efficiency is relatively low, and thus, it is difficult to meet the UAV photogrammetry’s requirements of convenience and speed. This paper proposes an Image-grouping and Self-Adaptive Patch-based Multi-View Stereo-matching algorithm (IG-SAPMVS) for multiple UAV imagery. First, multiple UAV images were grouped reasonably by a certain grouping strategy. Second, image dense matching was performed in each group and included three processes. (1) Initial feature-matching consists of two steps: The first was feature point detection and matching, which made some improvements to PMVS, according to the characteristics of UAV imagery. The second was edge point detection and matching, which aimed to control matching propagation during the expansion process; (2) The second process was matching propagation based on the self-adaptive patch. Initial patches were built that were centered by the obtained 3D seed points, and these were repeatedly expanded. The patches were prevented from crossing the discontinuous terrain by using the edge constraint, and the extent size and shape of the patches could automatically adapt to the terrain relief; (3) The third process was filtering the erroneous matching points. Taken the overlap problem between each group of 3D dense point clouds into account, the matching results were merged into a whole. Experiments conducted on three sets of typical UAV images with different texture features demonstrate that the proposed algorithm can address a large amount of UAV image data almost without computer memory restrictions, and the processing efficiency is significantly better than that of the PMVS algorithm and the matching accuracy is equal to that of the state-of-the-art PMVS algorithm. View Full-Text
Keywords: self-adaptive patch; image-grouping; multi-view stereo matching; unmanned aerial vehicle (UAV) imagery; Patch-based Multi-View Stereo matching (PMVS) self-adaptive patch; image-grouping; multi-view stereo matching; unmanned aerial vehicle (UAV) imagery; Patch-based Multi-View Stereo matching (PMVS)

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xiao, X.; Guo, B.; Li, D.; Li, L.; Yang, N.; Liu, J.; Zhang, P.; Peng, Z. Multi-View Stereo Matching Based on Self-Adaptive Patch and Image Grouping for Multiple Unmanned Aerial Vehicle Imagery. Remote Sens. 2016, 8, 89.

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