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 language | English (US) |
|---|---|
| Article number | 5308 |
| Pages (from-to) | 1-28 |
| Number of pages | 28 |
| Journal | Sensors (Switzerland) |
| Volume | 20 |
| Issue number | 18 |
| DOIs | |
| State | Published - Sep 2 2020 |
Funding
Funding: This work was supported in part by the Spanish Ministerio de Economía y Competitividad under contract DPI2016-77869-C2-2-R, by the Ministerio de Ciencia e Innovación under contract PID2019-105142RB-C22, and the Visiting Scholar Program at the University of Granada.
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