A survey of classical methods and new trends in pansharpening of multispectral images

By Israa Amro, Javier Mateos, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos. EURASIP Journal on Advances in Signal Processing, 2011

295 citations recorded by the source corpus.

There exist a number of satellites on different earth observation platforms, which provide multispectral images together with a panchromatic image, that is, an image containing reflectance data representative of a wide range of bands and wavelengths. Pansharpening is a pixel-level fusion technique used to increase the spatial resolution of the multispectral image while simultaneously preserving its spectral information. In this paper, we provide a review of the pan-sharpening methods proposed in the literature giving a clear classification of them and a description of their main characteristics. Finally, we analyze how the quality of the pansharpened images can be assessed both visually and quantitatively and examine the different quality measures proposed for that purpose.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/6920aac3e1be90c3f4b9e34f9afa1527b4719dcd

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In Computer Vision and Pattern Recognition

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Citations in the corpus

A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization (2014, background): “A related problem that has been extensively studied is pansharpening, which addresses the fusion of MSIs and panchromatic images (PANs), the latter of which are single-band images usually covering the visible and the near-infrared spectral ranges [4]–[6].”
Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation (2014, background): “FUSION of multisensor images has been explored during recent years and is still a very active research area [2].”
Hyperspectral Pansharpening: A Review (2015, background): “However, the definition of an assessment method that fulfills these constraints is still an open issue [80], [81], and closely relates to the general discussion regarding image quality assessment [82] and image fusion [83], [84].”
A Critical Comparison Among Pansharpening Algorithms (2015, background): “The group of methods defined by (5), varying with the choice of spectral weights in (2), is sometimes referred to as relative spectral contribution (RSC) [1], [6].”