A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening with Classical and Emerging Pansharpening Methods

Gemine Vivone, Mauro Dalla Mura, Andrea Garzelli, Rocco Restaino, Giuseppe Scarpa, Magnus O. Ulfarsson, Luciano Alparone, Jocelyn Chanussot

Research output: Contribution to specialist publicationArticle

3 Citations (Scopus)

Abstract

Pansharpening refers to the fusion of a multispectral (MS) image and panchromatic (PAN) data aimed at generating an outcome with the same spatial resolution of the PAN data and the spectral resolution of the MS image. In the last 30 years, several approaches to deal with this issue have been proposed. However, the reproducibility of these methods is often limited, making the comparison with the state of the art hard to achieve. Thus, to fill this gap, we propose a new benchmark consisting of recent advances in MS pansharpening. In particular, optimized classical approaches [multiresolution analysis (MRA) and component substitution (CS)] are compared with methods belonging to the third generation of pansharpening, represented by variational optimization-based (VO) and machine learning (ML) techniques. The benchmark is tested on different scenarios (from urban to rural areas) acquired by different commercial sensors [i.e., IKONOS (IK), GeoEye-1 (GE-1), and WorldView-3 (WV-3)]. Both quantitative and qualitative assessments and the computational burden are analyzed in this article, and all of the implementations have been collected in a MATLAB toolbox that is made available to the community.

Original languageEnglish
Pages53-81
Number of pages29
Volume9
No.1
Specialist publicationIEEE Geoscience and Remote Sensing Magazine
DOIs
Publication statusPublished - Mar 2021

Bibliographical note

Funding Information:
We gratefully acknowledge the support of the NVIDIA Corporation with the donation of the Titan XP GPU used for this research and the DigitalGlobe Foundation for providing the WorldView-3 NY data set.

Publisher Copyright:
© 2013 IEEE.

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