NDLI: A Precise Lower Bound on Image Subpixel Registration Accuracy

Content Provider	IEEE Xplore Digital Library
Author	Uss, M.L. Vozel, B. Dushepa, V.A. Komjak, V.A. Chehdi, K.
Copyright Year	1980
Abstract	A new performance bound is proposed for analyzing parametric image registration methods objectively. This original bound is derived from the Cramer-Rao lower bound on the estimation error of parameters involved in a geometric transformation assumed between reference and template images (pure translation in this work) and parameters describing the texture of these images. For describing local fragments of both the reference and the template images, the parametric fractional Brownian motion (fBm) model has been chosen. Experimental results, obtained first on pure fBm data with full matching of the data to the texture model assumption, give evidence that the proposed bound describes more adequately the performance of conventional estimators than two other bounds previously proposed in the literature. This holds with respect to the signal-to-noise ratio value of both images, the roughness of their texture, their correlation, and the actual value of translation parameters between their grids. Then, one real Hyperion hyperspectral data set is considered to test the proposed bound behavior on real data. The proposed bound is demonstrated to describe more adequately the estimation accuracy of the translation parameters between different bands of this data set.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	3333
Ending Page	3345
Page Count	13
File Size	1655357
File Format	PDF
ISSN	01962892
Volume Number	52
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Correlation Noise Vectors Interpolation Image registration Linear programming subpixel image registration Cramér–Rao lower bound (CRLB) error analysis Fisher information fractal Brownian motion model Hyperion hyperspectral imagery performance limits Cramér¿Rao lower bound (CRLB)
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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