A Study of Joint Histogram Estimation Methods to Register Multi-Sensor Remote Sensing Images Using Mutual Information

Hua Mei Chen, Pramod K. Varshney, Manoj K. Arora

Research output: Contribution to conferencePaper

6 Scopus citations

Abstract

Registration is the basic image processing operation in a variety of tasks such as multi-source classification, image fusion and change detection. Automatic intensity based registration techniques are gaining importance. In this paper, we investigate an intensity based technique that utilizes mutual information as the similarity measure. We apply this technique to perform multi-sensor image registration. The performance of a number of joint histogram estimation methods for the determination of mutual information has been evaluated using a measure called registration consistency. These methods include partial volume interpolation, cubic convolution interpolation, linear interpolation, and nearest neighborhood interpolation. The results show that partial volume interpolation produces the most reliable registration consistency. Nearest neighbor interpolation outperforms linear and cubic convolution interpolation.

Original languageEnglish (US)
Pages4035-4037
Number of pages3
StatePublished - Nov 24 2003
Event2003 IGARSS: Learning From Earth's Shapes and Colours - Toulouse, France
Duration: Jul 21 2003Jul 25 2003

Other

Other2003 IGARSS: Learning From Earth's Shapes and Colours
CountryFrance
CityToulouse
Period7/21/037/25/03

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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    Chen, H. M., Varshney, P. K., & Arora, M. K. (2003). A Study of Joint Histogram Estimation Methods to Register Multi-Sensor Remote Sensing Images Using Mutual Information. 4035-4037. Paper presented at 2003 IGARSS: Learning From Earth's Shapes and Colours, Toulouse, France.