A Critical Comparison Among Pansharpening Algorithms

• Published in: IEEE transactions on geoscience and remote sensing Vol. 53; no. 5; pp. 2565 - 2586
• Main Authors: Vivone, Gemine, Alparone, Luciano, Chanussot, Jocelyn, Dalla Mura, Mauro, Garzelli, Andrea, Licciardi, Giorgio A., Restaino, Rocco, Wald, Lucien
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-05-2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithm design and analysis; Algorithms; Assessments; Benchmarking; component substitution (CS); Computer simulation; Engineering Sciences; Indexes; Matlab; multiresolution analysis (MRA); multispectral (MS) pansharpening; Principal component analysis; quality assessment; Recognition; Regulatory agencies; Satellite-borne instruments; Spatial resolution; Transforms; Vectors; very high-resolution optical images; multiresolution analysis (MRA); Benchmarking; multispectral (MS) pansharpening; component substitution (CS); very high-resolution optical images; quality assessment
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2014.2361734

Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community.