Reliable Spatial Relationship Constrained Feature Point Matching of Oblique Aerial Images

2015 ◽  
Vol 81 (1) ◽  
pp. 49-58 ◽  
Author(s):  
Han Hu ◽  
Qing Zhu ◽  
Zhiqiang Du ◽  
Yeting Zhang ◽  
Yulin Ding
Keyword(s):  
Spatial Relationship ◽  
Feature Point ◽  
Aerial Images ◽  
Point Matching ◽  
Feature Point Matching

scholarly journals Feature Point Matching Method for Aerial Image Based on Recursive Diffusion Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 407
Author(s):  
Jiayan Shen ◽  
Xiucheng Guo ◽  
Wenzong Zhou ◽  
Yiming Zhang ◽  
Juchen Li
Keyword(s):  
Large Scale ◽  
High Density ◽  
Feature Point ◽  
Aerial Images ◽  
Image Feature ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Matching Accuracy ◽  
Point Matching ◽  
Feature Point Matching

Aerial images are large-scale and susceptible to light. Traditional image feature point matching algorithms cannot achieve satisfactory matching accuracy for aerial images. This paper proposes a recursive diffusion algorithm, which is scale-invariant and can be used to extract symmetrical areas of different images. This narrows the matching range of feature points by extracting high-density areas of the image and improving the matching accuracy through correlation analysis of high-density areas. Through experimental comparison, it can be found that the recursive diffusion algorithm has more advantages compared to the correlation coefficient method and the mean shift algorithm when matching accuracy of aerial images, especially when the light of aerial images changes greatly.

Feature-Point Matching for Aerial and Ground Images by Exploiting Line Segment-Based Local-Region Constraints

2021 ◽  
Vol 87 (10) ◽  
pp. 767-780
Author(s):  
Min Chen ◽  
Tong Fang ◽  
Qing Zhu ◽  
Xuming Ge ◽  
Zhanhao Zhang ◽  
...  
Keyword(s):  
Line Segment ◽  
Local Region ◽  
Feature Point ◽  
Aerial Images ◽  
Geometric Distortion ◽  
Matching Method ◽  
Point Matching ◽  
3D Mesh Model ◽  
Feature Point Matching ◽  
Segment Matching

In this study, we propose a feature-point matching method that is robust to viewpoint, scale, and illumination changes between aerial and ground images, to improve matching performance. First, a 3D rendering strategy is adopted to synthesize ground-view images from the 3D mesh model reconstructed from aerial images and overcome the global geometric distortion between aerial and ground images. We do not directly match feature points between the synthesized and ground images, but extract line-segment correspondences by designing a line-segment matching method that can adapt to the local geometric deformation, holes, and blurred textures on the synthesized image. Then, on the basis of the line-segment matches, local-region correspondences are constructed, and local regions on the synthesized image are propagated back to the original aerial images. Lastly, feature-point matching is performed between the aerial and ground images with the constraints of the local-region correspondences. Experimental results demonstrate that the proposed method can obtain more correct matches and higher matching precision than state-of-the-art methods. Specifically, the proposed method increases the average number of correct matches and average matching precision of the second-best method by more than five times and 40%, respectively.

Unsupervised multi-spectral image feature point matching under different lighting scenes

2021 ◽  
Author(s):  
Junchong Huang ◽  
Wei Tian ◽  
Yongkun Wen ◽  
Zhan Chen ◽  
Yuyao Huang
Keyword(s):  
Feature Point ◽  
Image Feature ◽  
Spectral Image ◽  
Point Matching ◽  
Feature Point Matching
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