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Remote Sens. 2017, 9(6), 581; doi:10.3390/rs9060581

Remote Sensing Image Registration Using Multiple Image Features

1,2,†
,
1,2,†
,
1,2,* , 1,3,* , 4
and
1,3
1
School of Information Science and Technology, Yunnan Normal University, Kunming 650092, China
2
The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China, Yunnan Normal University, Kunming 650092, China
3
Laboratory of Pattern Recognition and Artificial Intelligence, Yunnan Normal University, Kunming 650092, China
4
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 10 March 2017 / Revised: 27 May 2017 / Accepted: 4 June 2017 / Published: 12 June 2017
View Full-Text   |   Download PDF [20093 KB, uploaded 22 June 2017]   |  

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. View Full-Text
Keywords: remote sensing; image registration; multiple image features; different viewpoint; non-rigid distortion remote sensing; image registration; multiple image features; different viewpoint; non-rigid distortion
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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