Performance of Mutual Information Similarity Measure for Registration of Multitemporal Remote Sensing Images

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

Research output: Contribution to journalArticlepeer-review

177 Scopus citations


Accurate registration of multitemporal remote sensing images is essential for various change detection applications. Mutual information has recently been used as a similarity measure for registration of medical images because of its generality and high accuracy. Its application in remote sensing is relatively new. There are a number of algorithms for the estimation of joint histograms to compute mutual information, but they may suffer from interpolation-induced artifacts under certain conditions. In this paper, we investigate the use of a new joint histogram estimation algorithm called generalized partial volume estimation (GPVE) for computing mutual information to register multitemporal remote sensing images. The experimental results show that higher order GPVE algorithms have the ability to significantly reduce interpolation-induced artifacts. In addition, mutual-information-based image registration performed using the GPVE algorithm produces better registration consistency than the other two popular similarity measures, namely, mean squared difference (MSD) and normalized cross correlation (NCC), used for the registration of multitemporal remote sensing images.

Original languageEnglish (US)
Pages (from-to)2445-2454
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number11 PART I
StatePublished - Nov 2003


  • Image registration
  • Joint histogram estimation
  • Multitemporal images
  • Mutual information
  • Registration consistency

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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