Hyperspectral Pansharpening: A Review

By Laetitia Loncan, S. Fabre, Luís B. Almeida, José M. Bioucas-Dias, Wenzhi Liao, X. Briottet, Giorgio A. Licciardi, Jocelyn Chanussot, Miguel Simões, Nicolas Dobigeon and 5 more. IEEE Geoscience and Remote Sensing Magazine, 2015

778 citations recorded by the source corpus.

Pansharpening aims at fusing a panchromatic image with a multispectral one, to generate an image with the high spatial resolution of the former and the high spectral resolution of the latter. In the last decade, many algorithms have been presented in the literatures for pansharpening using multispectral data. With the increasing availability of hyperspectral systems, these methods are now being adapted to hyperspectral images. In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state-of-the-art methods for multispectral pansharpening, which have been adapted for hyperspectral data. Eleven methods from different classes (component substitution, multiresolution analysis, hybrid, Bayesian and matrix factorization) are analyzed. These methods are applied to three datasets and their effectiveness and robustness are evaluated with widely used performance indicators. In addition, all the pansharpening techniques considered in this paper have been implemented in a MATLAB toolbox that is made available to the community.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/72999a8336e68987a98456e3d43e02b6647c09e4

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Citations in the corpus

A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, methodology): “In [6], a review and a critical comparison are performed but for the problem of HS pansharpening, including methods in the CS, MRA, and VO classes.”
Camouflaged Object Detection with Feature Decomposition and Edge Reconstruction (2023, methodology): “Unlike existing heuristic-based feature aggregation strategies simply using concatenation [22, 24], GFA is specifically designed to address the key issue of COD, i.”
THE POPULATION OF GALAXY–GALAXY STRONG LENSES IN FORTHCOMING OPTICAL IMAGING SURVEYS (2015, background): “…can be found this way; it may be possible to use the near infra-red channels although they have decreased depth and resolution, or to use lower resolution multi-band optical data to add color information to the VIS imaging (e.g. Loncan et al. 2015), but we have not investigated these possibilities.”
SelfS2: Self-Supervised Transfer Learning for Sentinel-2 Multispectral Image Super-Resolution (2023, background): “However, as pointed out in [11] and [23], the super-resolution of S2 images differs from the conventional pansharpening problem [20], [24], [25], [26], [27], [28] and the hyperspectral image/MSI fusion problem [28], [29], [30], [31] mainly for the high-spatial-resolution bands (i.”
Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization (2018, background): “For an extensive comparison of hyperspectral pan-sharpening, see [16].”
Deep learning in remote sensing: a review (2017, background): “Traditionally, the field was dominated by works fusing multispectral data with panchromatic bands [117], but more recently it has been extended to thermal [118] or hyperspectral images [119].”
Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution (2019, background): “Digital Object Identiﬁer 10.1109/TNNLS.2018.2885616 resolution for existing cameras [9].”
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, methodology): “[81] also reviewed different pansharpening methods for generating high‐spatial resolution hyperspectral images.”
Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation (2016, background): “An overview of recent state-of-the-art hyperspectral and multispectral image fusion methods can be found in [39].”
A review of remote sensing image fusion methods (2016, background): “Authors in [1] studied and compared different fusion methods for hyperspectral data.”
Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation (2015, methodology): “In most practical scenarios, the spectral degradation L ∈ R n λ × m λ only depends on the spectral response of the sensor, which can be a priori known or estimated by cross-calibration [11].”
Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature (2017, background): “presented a comparative study of hyperspectral pan-sharpening, which is a special case of HS-MS fusion [10].”