Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest

TLDR

The two top-performing methods rely on multiresolution analysis and adaptive high‑pass detail injection. In January 2006, the IEEE Geoscience and Remote Sensing Society launched a public contest to identify the best pansharpening algorithms. Seven global research groups tested eight algorithms of various philosophies on complete QuickBird and simulated Pleiades datasets, with fusion results evaluated visually and objectively and quantitative assessment enabled by reference originals derived from higher‑resolution airborne data or degraded coarser data. The contest results, presented at the 2006 symposium, show that two algorithms outperform all others, a conclusion supported by both visual and quantitative evaluation. No additional information.

Abstract

In January 2006, the Data Fusion Committee of the IEEE Geoscience and Remote Sensing Society launched a public contest for pansharpening algorithms, which aimed to identify the ones that perform best. Seven research groups worldwide participated in the contest, testing eight algorithms following different philosophies [component substitution, multiresolution analysis (MRA), detail injection, etc. ]. Several complete data sets from two different sensors, namely, QuickBird and simulated Pleiades, were delivered to all participants. The fusion results were collected and evaluated, both visually and objectively. Quantitative results of pansharpening were possible owing to the availability of reference originals obtained either by simulating the data collected from the satellite sensor by means of higher resolution data from an airborne platform, in the case of the Pleiades data, or by first degrading all the available data to a coarser resolution and saving the original as the reference, in the case of the QuickBird data. The evaluation results were presented during the special session on data fusion at the 2006 international geoscience and remote sensing symposium in Denver, and these are discussed in further detail in this paper. Two algorithms outperform all the others, the visual analysis being confirmed by the quantitative evaluation. These two methods share the same philosophy: they basically rely on MRA and employ adaptive models for the injection of high-pass details.