A global quality measurement of pan-sharpened multispectral imagery

By Luciano Alparone, Stefano Baronti, Andrea Garzelli, Filippo Nencini. IEEE Geoscience and Remote Sensing Letters, 2004

626 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/cde90579cbee3ce13f7f86027821183024ca1e92

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

A survey of classical methods and new trends in pansharpening of multispectral images (2011, methodology): “For comparing the degraded and original low-resolution MS images, the Q4 index [113] is used.”
A Critical Comparison Among Pansharpening Algorithms (2015, background): “A vector extension of the Q-index to vector data up to four bands, which accounts also for spectral distortion, has been proposed in [74] .”
Improving Component Substitution Pansharpening Through Multivariate Regression of MS $+$Pan Data (2007, background): “Q4 takes into account the contrast, mean bias, and similarity between images [24].”
Hypercomplex Quality Assessment of Multi/Hyperspectral Images (2009, methodology): “hypercomplex CC of multivariate data [7] in order to measure”
A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter (2006, methodology): “Because all the fusion methods yielded rather steady plots for in [20], with was calculated and averaged on the entire image.”
Remote Sensing Image Fusion Based on Two-Stream Fusion Network (2017, background): “It is deﬁned as where σ ∗ and µ ∗ represents standard variance and mean of images, specially σ XY is the covariance of image X and Y. • Q 4 The Quality-index Q 4 [60] is the 4-band extension of Q index [56].”
Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images (2008, methodology): “…index for radiometric-distortion evaluation [8], whereas Q4, designed for four-band MS data, such as the QuickBird and Ikonos MS images, simultaneously accounts for the local mean bias, the changes in contrast, and the loss of correlation of individual bands, together with spectral distortion [24].”
Contrast and Error-Based Fusion Schemes for Multispectral Image Pansharpening (2014, methodology): “The assessment of the fused products (with respect to the reference image) is carried out by calculating different indexes [16] such as the Q4 index [17] and the ERGAS [13] to evaluate the global quality of images.”
Target-Adaptive CNN-Based Pansharpening (2017, neutral): “4) Q4/Q8: 4/8-band extension of Q [73].”
Remote sensing image fusion using the curvelet transform (2007, methodology): “The novel index Q4 [28] trades off both types of distortion and yields a unique quality index, according to which HPF is slightly better than AWL.”
Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest (2007, methodology): “An image quality index suitable for MS images having four spectral bands was recently proposed in [32] to assess the pan-sharpening methods.”
Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature (2017, background): “To overcome this limitation and additionally take into account spectral distortion, the Q4 index has been developed for four-band images by modeling each pixel vector as a quaternion [79].”
A New Pansharpening Algorithm Based on Total Variation (2014, methodology): “In this letter, we use the spectral angle mapper (SAM) [6], relative dimensionless global error synthesis (ERGAS) [7], Q4 [8], CC, and Qave as metrics that need a reference image and we use the QNR Dλ spectral [9] and QNR Ds spatial [9] metrics for quality assessment without reference.”
A New Pan-Sharpening Method Using a Compressed Sensing Technique (2011, background): “Smaller ERGAS indicates better fusion result (ideally zero). 5) The Q4 index is defined as [ 40 ]”
Pansharpening by Convolutional Neural Networks (2016, background): “SAM Spectral Angle Mapper [45] ERGAS Erreur Relative Globale Adimensionnelle de Synthèse [46] full reference SCC Spatial Correlation Coefficient [47] Q Universal Image Quality index [48] averaged over the bands Qx x-band extension of Q [49] QNR Quality with no-Reference index [44] no reference Dλ Sp”