Variational Bayesian Pansharpening with super-Gaussian sparse image priors

Fernando Pérez-Bueno*, Miguel Vega, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Pansharpening is a technique that fuses a low spatial resolution multispectral image and a high spatial resolution panchromatic one to obtain a multispectral image with the spatial resolution of the latter while preserving the spectral information of the multispectral image. In this paper we propose a variational Bayesian methodology for pansharpening. The proposed methodology uses the sensor characteristics to model the observation process and Super-Gaussian sparse image priors on the expected characteristics of the pansharpened image. The pansharpened image, as well as all model and variational parameters, are estimated within the proposed methodology. Using real and synthetic data, the quality of the pansharpened images is assessed both visually and quantitatively and compared with other pansharpening methods. Theoretical and experimental results demonstrate the effectiveness, efficiency, and flexibility of the proposed formulation.

Original languageEnglish (US)
Article number5308
Pages (from-to)1-28
Number of pages28
JournalSensors (Switzerland)
Volume20
Issue number18
DOIs
StatePublished - Sep 2 2020

Keywords

  • Image fusion
  • Pansharpening
  • Super-Gaussians
  • Variational Bayesian

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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