Registration of high-dimensional remote sensing data based on a new dimensionality reduction rule

Min Xu, Hao Chen, Pramod K. Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Registration of remote sensing data often involves dimensionality reduction of high-dimensional data to yield an image from each data set followed by pairwise image registration. We develop a new rule for dimensionality reduction such that the the Cramér-Rao lower bound (CRLB) for the estimation of the transformation parameters is minimized. A hyperspectral data set and a multispectral data set are used to evaluate our proposed rule. The experimental results using Mutual Information (MI) based pairwise registration technique demonstrate that our proposed rule can select the image pair with more texture, resulting in improved image registration results.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages581-584
Number of pages4
ISBN (Print)9781424456543
DOIs
StatePublished - Jan 1 2009
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: Nov 7 2009Nov 10 2009

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2009 IEEE International Conference on Image Processing, ICIP 2009
CountryEgypt
CityCairo
Period11/7/0911/10/09

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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    Xu, M., Chen, H., & Varshney, P. K. (2009). Registration of high-dimensional remote sensing data based on a new dimensionality reduction rule. In 2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings (pp. 581-584). [5413876] (Proceedings - International Conference on Image Processing, ICIP). IEEE Computer Society. https://doi.org/10.1109/ICIP.2009.5413876