NDLI: Geometrie feature-based image co-registration approach for InSAR

Content Provider	IEEE Xplore Digital Library
Author	Dong Li Yunhua Zhang
Copyright Year	2009
Description	Author affiliation: Center for Space Science and Applied Research, Chinese Academy of Science, Beijiig China (Yunhua Zhang) \|\| Graduate University of Chinese Academy of Sciences, Beijing China (Dong Li)
Abstract	This paper proposes a novel approach to co-register InSAR image pair. We use the SIFT descriptor to extract scale- and rotation-invariant point correspondences from images. Since the images are acquired spatial or temporal differently, there are unavoidable differences between them, which result in some mismatches in the correspondences. Generally, these mismatches must be removed so as to achieve a correct co-registration. However, this step is not necessary in our approach. Base on the obtained correspondences and the geometric transform relationship between images, a series of equations are formulated to calculate the samples of co-registration parameters: the rotation, scale, azimuth translation and range translation. We transform the parameter estimation problem into a linear regression problem, and the robust Least Median of Squares (LMedS) is introduced to obtain the precise value of co-registration parameters from these contaminated samples. Experiments show that the approach is able to co-register images with high precision and robustness even if substantial nonoverlapped areas exist between images.
Starting Page	1026
Ending Page	1030
File Size	267070
Page Count	5
File Format	PDF
ISBN	9781424427314
DOI	10.1109/APSAR.2009.5374252
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision support systems Parameter estimation SIFT Virtual reality InSAR Synthetic aperture radar interferometry Image co-registration
Content Type	Text
Resource Type	Article

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