Dense Image-Matching via Optical Flow Field Estimation and Fast-Guided Filter Refinement
Dense Image-Matching via Optical Flow Field Estimation and Fast-Guided Filter Refinement
复制标题
通过光流场估计和快速引导滤波器细化进行密集图像匹配
DOI：
10.3390/rs11202410
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发表时间：
2019-10
期刊：
影响因子：
5
通讯作者：
Shibasaki Ryosuke
中科院分区：
文献类型：
--
作者：
Yuan Wei;Yuan Xiuxiao;Xu Shu;Gong Jianya;Shibasaki Ryosuke
由于大面积航空图像的照明和地面特征的高度变化，开发一种高效、鲁棒的密集图像匹配方法一直是一个技术挑战。在本文中，我们提出了一种使用光流场和快速引导滤波器对航空图像进行密集匹配的方法。所提出的方法利用从粗到精的匹配策略来跨立体图像对进行像素级对应搜索。首先使用金字塔 Lucas-Kanade (L-K) 方法在立体图像对内生成稀疏光流场，然后使用调整后的控制格导出多级 B 样条插值函数以估计密集光流场。随后通过新颖的跨区域投票过程和快速引导过滤的结合来细化密集的对应关系。从匹配精度、匹配成功率和匹配效率三个方面评估该方法的性能。使用无人机（UAV）图像和航空数字测绘相机（DMC）图像进行评估实验。结果表明，该方法的重投影误差均方根误差（RMSE）优于图像±0.5像素，高度精度在距地面±2.5 GSD（地面采样距离）以内。该方法与最先进的商业软件SURE进行了进一步比较，证实该方法对纹理较差区域的图像能够提供更完整的匹配，该方法的匹配成功率高于97%，而SURE为96%，匹配效率高出47%。这证明了所提出的方法对于基于航空图像的不良纹理区域的密集匹配具有优异的适用性。
The development of an efficient and robust method for dense image-matching has been a technical challenge due to high variations in illumination and ground features of aerial images of large areas. In this paper, we propose a method for the dense matching of aerial images using an optical flow field and a fast-guided filter. The proposed method utilizes a coarse-to-fine matching strategy for a pixel-wise correspondence search across stereo image pairs. The pyramid Lucas–Kanade (L–K) method is first used to generate a sparse optical flow field within the stereo image pairs, and an adjusted control lattice is then used to derive the multi-level B-spline interpolating function for estimating the dense optical flow field. The dense correspondence is subsequently refined through a combination of a novel cross-region-based voting process and fast guided filtering. The performance of the proposed method was evaluated on three bases, namely, the matching accuracy, the matching success rate, and the matching efficiency. The evaluative experiments were performed using sets of unmanned aerial vehicle (UAV) images and aerial digital mapping camera (DMC) images. The results showed that the proposed method afforded the root mean square error (RMSE) of the reprojection errors better than ±0.5 pixels in image, and a height accuracy within ±2.5 GSD (ground sampling distance) from the ground. The method was further compared with the state-of-the-art commercial software SURE and confirmed to deliver more complete matches for images with poor-texture areas, the matching success rate of the proposed method is higher than 97% while SURE is 96%, and there is 47% higher matching efficiency. This demonstrates the superior applicability of the proposed method to aerial image-based dense matching with poor texture regions.
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DOI：
10.1016/0924-2716(95)00014-3
发表时间：
1996-04
影响因子：
12.7
作者：
E. Baltsavias
通讯作者：
E. Baltsavias
DOI：
10.1109/cvpr.2004.183
发表时间：
2004-06
期刊：
Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
影响因子：
--
作者：
Yan Ke;R. Sukthankar
通讯作者：
Yan Ke;R. Sukthankar
DOI：
10.1109/cvpr.2008.4587792
发表时间：
2008-06
期刊：
2008 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
D. Bradley;T. Boubekeur;W. Heidrich
通讯作者：
D. Bradley;T. Boubekeur;W. Heidrich
影响因子：
4.5
作者：
Hosni, Asmaa;Bleyer, Michael;Gelautz, Margrit
通讯作者：
Gelautz, Margrit
DOI：
10.5194/isprsarchives-xxxviii-4-w19-271-2011
发表时间：
2012-09
期刊：
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
影响因子：
--
作者：
M. Rothermel;N. Haala
通讯作者：
M. Rothermel;N. Haala