Preprint Article Version 1 This version not peer reviewed

Different Viewpoints Image Registration for Remote Sensing Based on Multiple Image Features

These authors contributed equally to this work.
Version 1 : Received: 2 May 2017 / Approved: 3 May 2017 / Online: 3 May 2017 (09:00:42 CEST)
Version 2 : Received: 9 June 2017 / Approved: 13 June 2017 / Online: 13 June 2017 (09:52:10 CEST)

A peer-reviewed article of this Preprint also exists.

Yang, K.; Pan, A.; Yang, Y.; Zhang, S.; Ong, S.H.; Tang, H. Remote Sensing Image Registration Using Multiple Image Features. Remote Sens. 2017, 9, 581. Yang, K.; Pan, A.; Yang, Y.; Zhang, S.; Ong, S.H.; Tang, H. Remote Sensing Image Registration Using Multiple Image Features. Remote Sens. 2017, 9, 581.

Journal reference: Remote Sens. 2017, 9, 581
DOI: 10.3390/rs9060581

Abstract

Remote sensing image registration with different viewpoints plays an important role in the field of geographic information system. However, when there exists ground relief variations and imaging viewpoint changes, non-rigid distortion occurs thus the registration becomes increasingly challenging. The current methods will suffer from missing true correspondences when non-rigid geometric distortion occurs. To address the problem, we propose a robust remote sensing image registration method based on SIFT feature distance and geometric structure features. At first, the scale-invariant feature transform (SIFT), a partial intensity invariant feature descriptor is used to extract reliable feature point set from sensed and reference image respectively. Secondly, a novel algorithm based on multiple image features which constrains the geometric structure during transformation is used to estimate exact correspondences between point sets. Finally, an accurate alignment is achieved by mapping the sensed image to reference image using thin-plate spline. We evaluated the performances of the proposed method by three sets of remote sensing images obtained from the unmanned aerial vehicle (UAV) and the Google earth, and compared with five state-of-the-art methods where our algorithm solved the non-rigid registration problem of remote sensing image with different viewpoints and showed the best alignments in most cases.

Subject Areas

remote sensing; image registration; multiple image features; different viewpoints; non-rigid distortion

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