ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume II-1/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-1/W1, 9–14, 2015
https://doi.org/10.5194/isprsannals-II-1-W1-9-2015
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-1/W1, 9–14, 2015
https://doi.org/10.5194/isprsannals-II-1-W1-9-2015

  27 Aug 2015

27 Aug 2015

A ROBUST MATCHING METHOD FOR UNMMANED AERIAL VEHICLE IMAGES WITH DIFFERENT VIEWPOINT ANGLES BASED ON REGIONAL COHERENCY

Z. Shao1,2, C. Li1,2, and N. Yang1,2 Z. Shao et al.
  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, No. 129 Luoyu Road, Wuhan, Hubei, China
  • 2Collaborative Innovation Center for Geospatial Technology,129 Luoyu Road, Wuhan 430079, China

Keywords: Unmanned Aerial Vehicle Images, Image Matching, Regional Coherency, Affine Invariant, Feature Detection, Feature Description

Abstract. 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.