Preprint Article Version 2 This version not peer reviewed

Remote Sensing Image Registration Using Multiple Image Features

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 plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

Subject Areas

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

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