A Multitemporal UAV Images Registration Approach Using Phase Congruency

If there are great illumination and contrast changes for multitemporal unmanned aerial vehicle (UA V) images, common image matching and registration methods may not work well on these images. To improve matching effects, this paper conduct the image registration using the structure consistency between the images to be matched, first, use phase congruency(PC) to describe images' structure characteristics and generate the PC images for both images to be matched; then, conduct the image matching by extracting the features from accelerated segment test (FAST) in the PC images; finally, use the method of randomized sample consensus (RANSAC) to eliminate the mismatches and obtain the affine transformation matrix between the two images. We use this method to pick out known control points on new UAV images on the basis of old image database of ground control points (GCP), the test proves that the method is independent of the radiation information and has a good effect on multitemporal UAV images registration.

[1]  F. Agüera,et al.  Surveying a Landslide in a Road Embankment Using Unmanned Aerial Vehicle Photogrammetry , 2012 .

[2]  Chengyi Wang,et al.  Unmanned aerial vehicle oblique image registration using an ASIFT-based matching method , 2018 .

[3]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[4]  Yan Wu,et al.  SAR and Optical Image Registration Using Nonlinear Diffusion and Phase Congruency Structural Descriptor , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Ethan Rublee,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[6]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[7]  Chung-Hsien Tsai,et al.  An accelerated image matching technique for UAV orthoimage registration , 2017 .

[8]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[10]  Shin'ichi Satoh Simple low-dimensional features approximating NCC-based image matching , 2011, Pattern Recognit. Lett..

[11]  Y. Ye,et al.  HOPC: A NOVEL SIMILARITY METRIC BASED ON GEOMETRIC STRUCTURAL PROPERTIES FOR MULTI-MODAL REMOTE SENSING IMAGE MATCHING , 2016 .

[12]  Peter Kovesi,et al.  Phase Congruency Detects Corners and Edges , 2003, DICTA.

[13]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[14]  A.V. Oppenheim,et al.  The importance of phase in signals , 1980, Proceedings of the IEEE.

[15]  Filiberto Chiabrando,et al.  DIRECT PHOTOGRAMMETRY USING UAV: TESTS AND FIRST RESULTS , 2013 .

[16]  Shuai Liu,et al.  Energy Spectrum CT Image Detection Based Dimensionality Reduction with Phase Congruency , 2018, Journal of Medical Systems.

[17]  Jean-Michel Morel,et al.  ASIFT: A New Framework for Fully Affine Invariant Image Comparison , 2009, SIAM J. Imaging Sci..

[18]  Peter Kovesi,et al.  Image Features from Phase Congruency , 1995 .