A Critical Comparison Among Pansharpening Algorithms

By Gemine Vivone, Luciano Alparone, Jocelyn Chanussot, M. Dalla Mura, Andrea Garzelli, Giorgio A. Licciardi, Rocco Restaino, Lucien Wald. IEEE Transactions on Geoscience and Remote Sensing, 2015

1,223 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/38328711715c407b300abdb2a9b173c600e96b0b

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Citations in the corpus

Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening (2021, methodology): “Furthermore, to evaluate the performance at full resolution, we employ the quality without reference (QNR), Dλ, and Ds indexes [6].”
Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band (2016, methodology): “This is because HPF and ATWT can preserve more spectral information of the original multispectral image [34].”
A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, methodology): “CONTRIBUTION Following the successful attempt developed by some of the authors about five years ago in [14], with this article, we move in the same direction, performing a critical new comparison.”
Remote Sensing Image Fusion Based on Two-Stream Fusion Network (2017, background): “Many research efforts have been devoted to developing pan-sharpening algorithms during the last decades [1, 2, 3].”
Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network (2017, methodology): “described using a simple detail injection framework [1].”
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, background): “Hence, there exists a tradeoff between spectral and spatial resolution [39], [40] [13, Chapter 9].”
Target-Adaptive CNN-Based Pansharpening (2017, methodology): “However, this CS-MRA dichotomy is useful to understand the behavior of any method falling in these categories as highlighted in [28], [29].”
Pixel-level image fusion: A survey of the state of the art (2017, methodology): “A critical comparison among the recently proposed remote sensing image fusion algorithms can be found in [8].”
Pansharpening by Convolutional Neural Networks (2016, methodology): “Therefore, to reduce the possible mismatches, we smooth data before downsampling, following [2,22], using a ﬁlter that matches the modulation transfer function of the sensor.”
Hyperspectral Pansharpening: A Review (2015, methodology): “The fusion step is summarized as [4], [39]”
SelfS2: Self-Supervised Transfer Learning for Sentinel-2 Multispectral Image Super-Resolution (2023, methodology): “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.”
A review of remote sensing image fusion methods (2016, methodology): “there is another formalization which is proposed and analyzed in [52] and [53].”
Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, methodology): “, 2003) Introducing the methods belonging to ARSIS, along with giving a simple comparison (Vivone et al., 2014) Giving a thorough descripitions and assessments of the methods belonging to CS and MRA families (Meng et al.”
A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening (2017, methodology): “[43] for providing the toolbox that helped us to implement five of the seven referenced algorithms, except TSSC and PNN.”
Forest Types Classification Based on Multi-Source Data Fusion (2017, methodology): “HSTAFM was tested using both the simulated and observed dataset, comparing with the other three state-of-the-art algorithms including the Spatial and Temporal Adaptive Reflectance Fusion model (STARFM), the Flexible Spatiotemporal Data Fusion Model (FSDAF) and the dictionary learning-based Spatiotem”
Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network (2018, background): “A recent review and comparison of pan-sharpening methods can be found in Vivone et al. (2015).”