A ROBUST MATCHING METHOD FOR UNMMANED AERIAL VEHICLE IMAGES WITH DIFFERENT VIEWPOINT ANGLES BASED ON REGIONAL COHERENCY
One of the main challenges confronting high-resolution remote sensing image matching is how to address the issue of geometric deformation between images, especially when the images are obtained from different viewpoints. In this paper, a robust matching method for Unmanned Aerial Vehicle images of d...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2015-08-01
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Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-1-W1/9/2015/isprsannals-II-1-W1-9-2015.pdf |
Summary: | One of the main challenges confronting high-resolution remote sensing image matching is how to address the issue of geometric
deformation between images, especially when the images are obtained from different viewpoints. In this paper, a robust matching
method for Unmanned Aerial Vehicle images of different viewpoint angles based on regional coherency is proposed. The literature
on the geometric transform analysis reveals that if transformations between different pixel pairs are different, they can't be expressed
by a uniform affine transform. While for the same real scene, if the instantaneous field of view or the target depth changes is small,
transformation between pixels in the whole image can be approximated by an affine transform. On the basis of this analysis, a region
coherency matching method for Unmanned Aerial Vehicle images is proposed. In the proposed method, the simplified mapping
from image view change to scale change and rotation change has been derived. Through this processing, the matching between view
change images can be converted into the matching between rotation and scale changed images. In the method, firstly local image
regions are detected and view changes between these local regions are mapped to rotation and scale change by performing local
region simulation. And then, point feature detection and matching are implemented in the simulated image regions. Finally, a group
of Unmanned Aerial Vehicle images are adopted to verify the performance of proposed matching method respectively, and a
comparative analysis with other methods demonstrates the effectiveness of the proposed method. |
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ISSN: | 2194-9042 2194-9050 |